In the following code, we will import fbeta_score,make_scorer from sklearn.metrics by which that require probability evaluation of the positive class. ~~ If current p score is better than the score of last choice of it, we store current p, say best_params. Python SCORERS - 5 examples found. I think GridSearchCV() should support clustering estimators as well. What is the motivation of using cross-validation in this setting? Same issue holds true for DBSCAN. There is no notion of training and test set in your code, And the way you define training and test score are confusing. sklearn.metrics is a function that implements score, probability functions to calculate classification performance. at Keras) or writing your own estimator. a scorer callable object / function with signature. Example: Gaussian process regression with noise-level estimation, Example: Gaussian processes on discrete data structures, Example: Gradient Boosting Out-of-Bag estimates, Example: Gradient Boosting regularization, Example: Hashing feature transformation using Totally Random Trees, Example: HuberRegressor vs Ridge on dataset with strong outliers, Example: Illustration of Gaussian process classification on the XOR dataset, Example: Illustration of prior and posterior Gaussian process for different kernels, Example: Image denoising using dictionary learning, Example: Imputing missing values before building an estimator, Example: Imputing missing values with variants of IterativeImputer, Example: Iso-probability lines for Gaussian Processes classification, Example: Joint feature selection with multi-task Lasso, Example: Kernel Density Estimate of Species Distributions, Example: L1 Penalty and Sparsity in Logistic Regression, Example: Label Propagation digits active learning, Example: Label Propagation learning a complex structure, Example: Lasso and Elastic Net for Sparse Signals, Example: Linear and Quadratic Discriminant Analysis with covariance ellipsoid, Example: Logistic Regression 3-class Classifier, Example: MNIST classification using multinomial logistic + L1, Example: Manifold Learning methods on a severed sphere, Example: Manifold learning on handwritten digits, Example: Map data to a normal distribution, Example: Model selection with Probabilistic PCA and Factor Analysis, Example: Model-based and sequential feature selection, Example: Multi-class AdaBoosted Decision Trees, Example: Multi-output Decision Tree Regression, Example: Multiclass sparse logistic regression on 20newgroups, Example: Nearest Neighbors Classification, Example: Neighborhood Components Analysis Illustration, Example: Nested versus non-nested cross-validation, Example: Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification, Example: Novelty detection with Local Outlier Factor, Example: One-class SVM with non-linear kernel, Example: Online learning of a dictionary of parts of faces, Example: Ordinary Least Squares and Ridge Regression Variance, Example: Out-of-core classification of text documents, Example: Outlier detection on a real data set, Example: Outlier detection with Local Outlier Factor, Example: Parameter estimation using grid search with cross-validation, Example: Partial Dependence and Individual Conditional Expectation Plots, Example: Permutation Importance vs Random Forest Feature Importance, Example: Permutation Importance with Multicollinear or Correlated Features, Example: Pixel importances with a parallel forest of trees, Example: Plot Hierarchical Clustering Dendrogram, Example: Plot Ridge coefficients as a function of the L2 regularization, Example: Plot Ridge coefficients as a function of the regularization, Example: Plot class probabilities calculated by the VotingClassifier, Example: Plot different SVM classifiers in the iris dataset, Example: Plot individual and voting regression predictions, Example: Plot multi-class SGD on the iris dataset, Example: Plot multinomial and One-vs-Rest Logistic Regression, Example: Plot randomly generated classification dataset, Example: Plot randomly generated multilabel dataset, Example: Plot the decision boundaries of a VotingClassifier, Example: Plot the decision surface of a decision tree on the iris dataset, Example: Plot the decision surfaces of ensembles of trees on the iris dataset, Example: Plot the support vectors in LinearSVC, Example: Plotting Cross-Validated Predictions, Example: Poisson regression and non-normal loss, Example: Post pruning decision trees with cost complexity pruning, Example: Prediction Intervals for Gradient Boosting Regression, Example: Principal Component Regression vs Partial Least Squares Regression, Example: Probabilistic predictions with Gaussian process classification, Example: Probability Calibration for 3-class classification, Example: Probability calibration of classifiers, Example: ROC Curve with Visualization API, Example: Receiver Operating Characteristic, Example: Receiver Operating Characteristic with cross validation, Example: Recursive feature elimination with cross-validation, Example: Regularization path of L1- Logistic Regression, Example: Release Highlights for scikit-learn 0.22, Example: Release Highlights for scikit-learn 0.23, Example: Release Highlights for scikit-learn 0.24, Example: Restricted Boltzmann Machine features for digit classification, Example: Robust covariance estimation and Mahalanobis distances relevance, Example: Robust linear model estimation using RANSAC, Example: Robust vs Empirical covariance estimate, Example: SGD: Maximum margin separating hyperplane, Example: SVM: Maximum margin separating hyperplane, Example: SVM: Separating hyperplane for unbalanced classes, Example: Sample pipeline for text feature extraction and evaluation, Example: Scalable learning with polynomial kernel aproximation, Example: Scaling the regularization parameter for SVCs, Example: Segmenting the picture of greek coins in regions, Example: Selecting dimensionality reduction with Pipeline and GridSearchCV, Example: Selecting the number of clusters with silhouette analysis on KMeans clustering, Example: Semi-supervised Classification on a Text Dataset, Example: Simple 1D Kernel Density Estimation, Example: Sparse coding with a precomputed dictionary, Example: Sparse inverse covariance estimation, Example: Spectral clustering for image segmentation, Example: Statistical comparison of models using grid search, Example: Support Vector Regression using linear and non-linear kernels, Example: Test with permutations the significance of a classification score, Example: The Johnson-Lindenstrauss bound for embedding with random projections, Example: Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation, Example: Tweedie regression on insurance claims, Example: Understanding the decision tree structure, Example: Using KBinsDiscretizer to discretize continuous features, Example: Various Agglomerative Clustering on a 2D embedding of digits, Example: Varying regularization in Multi-layer Perceptron, Example: Visualization of MLP weights on MNIST, Example: Visualizations with Display Objects, Example: Visualizing cross-validation behavior in scikit-learn, Example: Visualizing the stock market structure, Example: t-SNE: The effect of various perplexity values on the shape, calibration.CalibratedClassifierCV.get_params(), calibration.CalibratedClassifierCV.predict(), calibration.CalibratedClassifierCV.predict_proba(), calibration.CalibratedClassifierCV.score(), calibration.CalibratedClassifierCV.set_params(), cluster.AffinityPropagation.fit_predict(), cluster.AgglomerativeClustering.fit_predict(), cluster.AgglomerativeClustering.get_params(), cluster.AgglomerativeClustering.set_params(), cluster.FeatureAgglomeration.fit_predict(), cluster.FeatureAgglomeration.fit_transform(), cluster.FeatureAgglomeration.get_params(), cluster.FeatureAgglomeration.inverse_transform(), cluster.FeatureAgglomeration.set_params(), cluster.SpectralBiclustering.biclusters_(), cluster.SpectralBiclustering.get_indices(), cluster.SpectralBiclustering.get_params(), cluster.SpectralBiclustering.get_submatrix(), cluster.SpectralBiclustering.set_params(), cluster.SpectralCoclustering.biclusters_(), cluster.SpectralCoclustering.get_indices(), cluster.SpectralCoclustering.get_params(), cluster.SpectralCoclustering.get_submatrix(), cluster.SpectralCoclustering.set_params(), compose.ColumnTransformer.fit_transform(), compose.ColumnTransformer.get_feature_names(), compose.ColumnTransformer.named_transformers_(), compose.TransformedTargetRegressor.get_params(), compose.TransformedTargetRegressor.predict(), compose.TransformedTargetRegressor.score(), compose.TransformedTargetRegressor.set_params(), sklearn.compose.make_column_transformer(), covariance.EllipticEnvelope.correct_covariance(), covariance.EllipticEnvelope.decision_function(), covariance.EllipticEnvelope.fit_predict(), covariance.EllipticEnvelope.get_precision(), covariance.EllipticEnvelope.mahalanobis(), covariance.EllipticEnvelope.reweight_covariance(), covariance.EllipticEnvelope.score_samples(), covariance.EmpiricalCovariance.error_norm(), covariance.EmpiricalCovariance.get_params(), covariance.EmpiricalCovariance.get_precision(), covariance.EmpiricalCovariance.mahalanobis(), covariance.EmpiricalCovariance.set_params(), covariance.GraphicalLasso.get_precision(), covariance.GraphicalLassoCV.get_precision(), covariance.GraphicalLassoCV.mahalanobis(), covariance.MinCovDet.correct_covariance(), covariance.MinCovDet.reweight_covariance(), covariance.ShrunkCovariance.get_precision(), covariance.ShrunkCovariance.mahalanobis(), sklearn.covariance.empirical_covariance(), cross_decomposition.CCA.inverse_transform(), cross_decomposition.PLSCanonical.fit_transform(), cross_decomposition.PLSCanonical.get_params(), cross_decomposition.PLSCanonical.inverse_transform(), cross_decomposition.PLSCanonical.predict(), cross_decomposition.PLSCanonical.set_params(), cross_decomposition.PLSCanonical.transform(), cross_decomposition.PLSRegression.fit_transform(), cross_decomposition.PLSRegression.get_params(), cross_decomposition.PLSRegression.inverse_transform(), cross_decomposition.PLSRegression.predict(), cross_decomposition.PLSRegression.score(), cross_decomposition.PLSRegression.set_params(), cross_decomposition.PLSRegression.transform(), cross_decomposition.PLSSVD.fit_transform(), datasets.make_multilabel_classification(), sklearn.datasets.fetch_20newsgroups_vectorized(), sklearn.datasets.fetch_california_housing(), sklearn.datasets.fetch_species_distributions(), sklearn.datasets.make_gaussian_quantiles(), sklearn.datasets.make_multilabel_classification(), sklearn.datasets.make_sparse_coded_signal(), sklearn.datasets.make_sparse_spd_matrix(), sklearn.datasets.make_sparse_uncorrelated(), decomposition.DictionaryLearning.fit_transform(), decomposition.DictionaryLearning.get_params(), decomposition.DictionaryLearning.set_params(), decomposition.DictionaryLearning.transform(), decomposition.FactorAnalysis.fit_transform(), decomposition.FactorAnalysis.get_covariance(), decomposition.FactorAnalysis.get_params(), decomposition.FactorAnalysis.get_precision(), decomposition.FactorAnalysis.score_samples(), decomposition.FactorAnalysis.set_params(), decomposition.FastICA.inverse_transform(), decomposition.IncrementalPCA.fit_transform(), decomposition.IncrementalPCA.get_covariance(), decomposition.IncrementalPCA.get_params(), decomposition.IncrementalPCA.get_precision(), decomposition.IncrementalPCA.inverse_transform(), decomposition.IncrementalPCA.partial_fit(), decomposition.IncrementalPCA.set_params(), decomposition.KernelPCA.inverse_transform(), decomposition.LatentDirichletAllocation(), decomposition.LatentDirichletAllocation.fit(), decomposition.LatentDirichletAllocation.fit_transform(), decomposition.LatentDirichletAllocation.get_params(), decomposition.LatentDirichletAllocation.partial_fit(), decomposition.LatentDirichletAllocation.perplexity(), decomposition.LatentDirichletAllocation.score(), decomposition.LatentDirichletAllocation.set_params(), decomposition.LatentDirichletAllocation.transform(), decomposition.MiniBatchDictionaryLearning, decomposition.MiniBatchDictionaryLearning(), decomposition.MiniBatchDictionaryLearning.fit(), decomposition.MiniBatchDictionaryLearning.fit_transform(), decomposition.MiniBatchDictionaryLearning.get_params(), decomposition.MiniBatchDictionaryLearning.partial_fit(), decomposition.MiniBatchDictionaryLearning.set_params(), decomposition.MiniBatchDictionaryLearning.transform(), decomposition.MiniBatchSparsePCA.fit_transform(), decomposition.MiniBatchSparsePCA.get_params(), decomposition.MiniBatchSparsePCA.set_params(), decomposition.MiniBatchSparsePCA.transform(), decomposition.SparseCoder.fit_transform(), decomposition.TruncatedSVD.fit_transform(), decomposition.TruncatedSVD.inverse_transform(), decomposition.non_negative_factorization(), sklearn.decomposition.dict_learning_online(), sklearn.decomposition.non_negative_factorization(), discriminant_analysis.LinearDiscriminantAnalysis, discriminant_analysis.LinearDiscriminantAnalysis(), discriminant_analysis.LinearDiscriminantAnalysis.decision_function(), discriminant_analysis.LinearDiscriminantAnalysis.fit(), discriminant_analysis.LinearDiscriminantAnalysis.fit_transform(), discriminant_analysis.LinearDiscriminantAnalysis.get_params(), discriminant_analysis.LinearDiscriminantAnalysis.predict(), discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba(), discriminant_analysis.LinearDiscriminantAnalysis.predict_proba(), discriminant_analysis.LinearDiscriminantAnalysis.score(), discriminant_analysis.LinearDiscriminantAnalysis.set_params(), discriminant_analysis.LinearDiscriminantAnalysis.transform(), discriminant_analysis.QuadraticDiscriminantAnalysis, discriminant_analysis.QuadraticDiscriminantAnalysis(), discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function(), discriminant_analysis.QuadraticDiscriminantAnalysis.fit(), discriminant_analysis.QuadraticDiscriminantAnalysis.get_params(), discriminant_analysis.QuadraticDiscriminantAnalysis.predict(), discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba(), discriminant_analysis.QuadraticDiscriminantAnalysis.predict_proba(), discriminant_analysis.QuadraticDiscriminantAnalysis.score(), discriminant_analysis.QuadraticDiscriminantAnalysis.set_params(), dummy.DummyClassifier.predict_log_proba(), ensemble.AdaBoostClassifier.decision_function(), ensemble.AdaBoostClassifier.feature_importances_(), ensemble.AdaBoostClassifier.predict_log_proba(), ensemble.AdaBoostClassifier.predict_proba(), ensemble.AdaBoostClassifier.staged_decision_function(), ensemble.AdaBoostClassifier.staged_predict(), ensemble.AdaBoostClassifier.staged_predict_proba(), ensemble.AdaBoostClassifier.staged_score(), ensemble.AdaBoostRegressor.feature_importances_(), ensemble.AdaBoostRegressor.staged_predict(), ensemble.AdaBoostRegressor.staged_score(), ensemble.BaggingClassifier.decision_function(), ensemble.BaggingClassifier.estimators_samples_(), ensemble.BaggingClassifier.predict_log_proba(), ensemble.BaggingClassifier.predict_proba(), ensemble.BaggingRegressor.estimators_samples_(), ensemble.ExtraTreesClassifier.decision_path(), ensemble.ExtraTreesClassifier.feature_importances_(), ensemble.ExtraTreesClassifier.get_params(), ensemble.ExtraTreesClassifier.predict_log_proba(), ensemble.ExtraTreesClassifier.predict_proba(), ensemble.ExtraTreesClassifier.set_params(), ensemble.ExtraTreesRegressor.decision_path(), ensemble.ExtraTreesRegressor.feature_importances_(), ensemble.ExtraTreesRegressor.get_params(), ensemble.ExtraTreesRegressor.set_params(), ensemble.GradientBoostingClassifier.apply(), ensemble.GradientBoostingClassifier.decision_function(), ensemble.GradientBoostingClassifier.feature_importances_(), ensemble.GradientBoostingClassifier.fit(), ensemble.GradientBoostingClassifier.get_params(), ensemble.GradientBoostingClassifier.predict(), ensemble.GradientBoostingClassifier.predict_log_proba(), ensemble.GradientBoostingClassifier.predict_proba(), ensemble.GradientBoostingClassifier.score(), ensemble.GradientBoostingClassifier.set_params(), ensemble.GradientBoostingClassifier.staged_decision_function(), ensemble.GradientBoostingClassifier.staged_predict(), ensemble.GradientBoostingClassifier.staged_predict_proba(), ensemble.GradientBoostingRegressor.apply(), ensemble.GradientBoostingRegressor.feature_importances_(), ensemble.GradientBoostingRegressor.get_params(), ensemble.GradientBoostingRegressor.predict(), ensemble.GradientBoostingRegressor.score(), ensemble.GradientBoostingRegressor.set_params(), ensemble.GradientBoostingRegressor.staged_predict(), ensemble.HistGradientBoostingClassifier(), ensemble.HistGradientBoostingClassifier.decision_function(), ensemble.HistGradientBoostingClassifier.fit(), ensemble.HistGradientBoostingClassifier.get_params(), ensemble.HistGradientBoostingClassifier.predict(), ensemble.HistGradientBoostingClassifier.predict_proba(), ensemble.HistGradientBoostingClassifier.score(), ensemble.HistGradientBoostingClassifier.set_params(), ensemble.HistGradientBoostingClassifier.staged_decision_function(), ensemble.HistGradientBoostingClassifier.staged_predict(), ensemble.HistGradientBoostingClassifier.staged_predict_proba(), ensemble.HistGradientBoostingRegressor.fit(), ensemble.HistGradientBoostingRegressor.get_params(), ensemble.HistGradientBoostingRegressor.predict(), ensemble.HistGradientBoostingRegressor.score(), ensemble.HistGradientBoostingRegressor.set_params(), ensemble.HistGradientBoostingRegressor.staged_predict(), ensemble.IsolationForest.decision_function(), ensemble.IsolationForest.estimators_samples_(), ensemble.RandomForestClassifier.decision_path(), ensemble.RandomForestClassifier.feature_importances_(), ensemble.RandomForestClassifier.get_params(), ensemble.RandomForestClassifier.predict(), ensemble.RandomForestClassifier.predict_log_proba(), ensemble.RandomForestClassifier.predict_proba(), ensemble.RandomForestClassifier.set_params(), ensemble.RandomForestRegressor.decision_path(), ensemble.RandomForestRegressor.feature_importances_(), ensemble.RandomForestRegressor.get_params(), ensemble.RandomForestRegressor.set_params(), ensemble.RandomTreesEmbedding.decision_path(), ensemble.RandomTreesEmbedding.feature_importances_(), ensemble.RandomTreesEmbedding.fit_transform(), ensemble.RandomTreesEmbedding.get_params(), ensemble.RandomTreesEmbedding.set_params(), ensemble.RandomTreesEmbedding.transform(), ensemble.StackingClassifier.decision_function(), ensemble.StackingClassifier.fit_transform(), ensemble.StackingClassifier.n_features_in_(), ensemble.StackingClassifier.predict_proba(), ensemble.StackingRegressor.fit_transform(), ensemble.StackingRegressor.n_features_in_(), ensemble.VotingClassifier.fit_transform(), ensemble.VotingClassifier.predict_proba(), exceptions.ConvergenceWarning.with_traceback(), exceptions.DataConversionWarning.with_traceback(), exceptions.DataDimensionalityWarning.with_traceback(), exceptions.EfficiencyWarning.with_traceback(), exceptions.FitFailedWarning.with_traceback(), exceptions.NotFittedError.with_traceback(), exceptions.UndefinedMetricWarning.with_traceback(), feature_extraction.DictVectorizer.fit_transform(), feature_extraction.DictVectorizer.get_feature_names(), feature_extraction.DictVectorizer.get_params(), feature_extraction.DictVectorizer.inverse_transform(), feature_extraction.DictVectorizer.restrict(), feature_extraction.DictVectorizer.set_params(), feature_extraction.DictVectorizer.transform(), feature_extraction.FeatureHasher.fit_transform(), feature_extraction.FeatureHasher.get_params(), feature_extraction.FeatureHasher.set_params(), feature_extraction.FeatureHasher.transform(), feature_extraction.image.PatchExtractor(), feature_extraction.image.PatchExtractor.fit(), feature_extraction.image.PatchExtractor.get_params(), feature_extraction.image.PatchExtractor.set_params(), feature_extraction.image.PatchExtractor.transform(), feature_extraction.image.extract_patches_2d(), feature_extraction.image.reconstruct_from_patches_2d(), sklearn.feature_extraction.image.extract_patches_2d(), sklearn.feature_extraction.image.grid_to_graph(), sklearn.feature_extraction.image.img_to_graph(), sklearn.feature_extraction.image.reconstruct_from_patches_2d(), feature_extraction.text.CountVectorizer(), feature_extraction.text.CountVectorizer.build_analyzer(), feature_extraction.text.CountVectorizer.build_preprocessor(), feature_extraction.text.CountVectorizer.build_tokenizer(), feature_extraction.text.CountVectorizer.decode(), feature_extraction.text.CountVectorizer.fit(), feature_extraction.text.CountVectorizer.fit_transform(), feature_extraction.text.CountVectorizer.get_feature_names(), feature_extraction.text.CountVectorizer.get_params(), feature_extraction.text.CountVectorizer.get_stop_words(), feature_extraction.text.CountVectorizer.inverse_transform(), feature_extraction.text.CountVectorizer.set_params(), feature_extraction.text.CountVectorizer.transform(), feature_extraction.text.HashingVectorizer, feature_extraction.text.HashingVectorizer(), feature_extraction.text.HashingVectorizer.build_analyzer(), feature_extraction.text.HashingVectorizer.build_preprocessor(), feature_extraction.text.HashingVectorizer.build_tokenizer(), feature_extraction.text.HashingVectorizer.decode(), feature_extraction.text.HashingVectorizer.fit(), feature_extraction.text.HashingVectorizer.fit_transform(), feature_extraction.text.HashingVectorizer.get_params(), feature_extraction.text.HashingVectorizer.get_stop_words(), feature_extraction.text.HashingVectorizer.partial_fit(), feature_extraction.text.HashingVectorizer.set_params(), feature_extraction.text.HashingVectorizer.transform(), feature_extraction.text.TfidfTransformer(), feature_extraction.text.TfidfTransformer.fit(), feature_extraction.text.TfidfTransformer.fit_transform(), feature_extraction.text.TfidfTransformer.get_params(), feature_extraction.text.TfidfTransformer.set_params(), feature_extraction.text.TfidfTransformer.transform(), feature_extraction.text.TfidfVectorizer(), feature_extraction.text.TfidfVectorizer.build_analyzer(), feature_extraction.text.TfidfVectorizer.build_preprocessor(), feature_extraction.text.TfidfVectorizer.build_tokenizer(), feature_extraction.text.TfidfVectorizer.decode(), feature_extraction.text.TfidfVectorizer.fit(), feature_extraction.text.TfidfVectorizer.fit_transform(), feature_extraction.text.TfidfVectorizer.get_feature_names(), feature_extraction.text.TfidfVectorizer.get_params(), feature_extraction.text.TfidfVectorizer.get_stop_words(), feature_extraction.text.TfidfVectorizer.inverse_transform(), feature_extraction.text.TfidfVectorizer.set_params(), feature_extraction.text.TfidfVectorizer.transform(), feature_selection.GenericUnivariateSelect, feature_selection.GenericUnivariateSelect(), feature_selection.GenericUnivariateSelect.fit(), feature_selection.GenericUnivariateSelect.fit_transform(), feature_selection.GenericUnivariateSelect.get_params(), feature_selection.GenericUnivariateSelect.get_support(), feature_selection.GenericUnivariateSelect.inverse_transform(), feature_selection.GenericUnivariateSelect.set_params(), feature_selection.GenericUnivariateSelect.transform(), feature_selection.RFE.decision_function(), feature_selection.RFE.inverse_transform(), feature_selection.RFE.predict_log_proba(), feature_selection.RFECV.decision_function(), feature_selection.RFECV.inverse_transform(), feature_selection.RFECV.predict_log_proba(), feature_selection.SelectFdr.fit_transform(), feature_selection.SelectFdr.get_support(), feature_selection.SelectFdr.inverse_transform(), feature_selection.SelectFpr.fit_transform(), feature_selection.SelectFpr.get_support(), feature_selection.SelectFpr.inverse_transform(), feature_selection.SelectFromModel.fit_transform(), feature_selection.SelectFromModel.get_params(), feature_selection.SelectFromModel.get_support(), feature_selection.SelectFromModel.inverse_transform(), feature_selection.SelectFromModel.partial_fit(), feature_selection.SelectFromModel.set_params(), feature_selection.SelectFromModel.transform(), feature_selection.SelectFwe.fit_transform(), feature_selection.SelectFwe.get_support(), feature_selection.SelectFwe.inverse_transform(), feature_selection.SelectKBest.fit_transform(), feature_selection.SelectKBest.get_params(), feature_selection.SelectKBest.get_support(), feature_selection.SelectKBest.inverse_transform(), feature_selection.SelectKBest.set_params(), feature_selection.SelectKBest.transform(), feature_selection.SelectPercentile.fit_transform(), feature_selection.SelectPercentile.get_params(), feature_selection.SelectPercentile.get_support(), feature_selection.SelectPercentile.inverse_transform(), feature_selection.SelectPercentile.set_params(), feature_selection.SelectPercentile.transform(), feature_selection.SelectorMixin.fit_transform(), feature_selection.SelectorMixin.get_support(), feature_selection.SelectorMixin.inverse_transform(), feature_selection.SelectorMixin.transform(), feature_selection.SequentialFeatureSelector, feature_selection.SequentialFeatureSelector(), feature_selection.SequentialFeatureSelector.fit(), feature_selection.SequentialFeatureSelector.fit_transform(), feature_selection.SequentialFeatureSelector.get_params(), feature_selection.SequentialFeatureSelector.get_support(), feature_selection.SequentialFeatureSelector.inverse_transform(), feature_selection.SequentialFeatureSelector.set_params(), feature_selection.SequentialFeatureSelector.transform(), feature_selection.VarianceThreshold.fit(), feature_selection.VarianceThreshold.fit_transform(), feature_selection.VarianceThreshold.get_params(), feature_selection.VarianceThreshold.get_support(), feature_selection.VarianceThreshold.inverse_transform(), feature_selection.VarianceThreshold.set_params(), feature_selection.VarianceThreshold.transform(), feature_selection.mutual_info_regression(), sklearn.feature_selection.mutual_info_classif(), sklearn.feature_selection.mutual_info_regression(), gaussian_process.GaussianProcessClassifier, gaussian_process.GaussianProcessClassifier(), gaussian_process.GaussianProcessClassifier.fit(), gaussian_process.GaussianProcessClassifier.get_params(), gaussian_process.GaussianProcessClassifier.log_marginal_likelihood(), gaussian_process.GaussianProcessClassifier.predict(), gaussian_process.GaussianProcessClassifier.predict_proba(), gaussian_process.GaussianProcessClassifier.score(), gaussian_process.GaussianProcessClassifier.set_params(), gaussian_process.GaussianProcessRegressor, gaussian_process.GaussianProcessRegressor(), gaussian_process.GaussianProcessRegressor.fit(), gaussian_process.GaussianProcessRegressor.get_params(), gaussian_process.GaussianProcessRegressor.log_marginal_likelihood(), gaussian_process.GaussianProcessRegressor.predict(), gaussian_process.GaussianProcessRegressor.sample_y(), gaussian_process.GaussianProcessRegressor.score(), gaussian_process.GaussianProcessRegressor.set_params(), gaussian_process.kernels.CompoundKernel(), gaussian_process.kernels.CompoundKernel.__call__(), gaussian_process.kernels.CompoundKernel.bounds(), gaussian_process.kernels.CompoundKernel.clone_with_theta(), gaussian_process.kernels.CompoundKernel.diag(), gaussian_process.kernels.CompoundKernel.get_params(), gaussian_process.kernels.CompoundKernel.hyperparameters(), gaussian_process.kernels.CompoundKernel.is_stationary(), gaussian_process.kernels.CompoundKernel.n_dims(), gaussian_process.kernels.CompoundKernel.requires_vector_input(), gaussian_process.kernels.CompoundKernel.set_params(), gaussian_process.kernels.CompoundKernel.theta(), gaussian_process.kernels.ConstantKernel(), gaussian_process.kernels.ConstantKernel.__call__(), gaussian_process.kernels.ConstantKernel.bounds(), gaussian_process.kernels.ConstantKernel.clone_with_theta(), gaussian_process.kernels.ConstantKernel.diag(), gaussian_process.kernels.ConstantKernel.get_params(), gaussian_process.kernels.ConstantKernel.hyperparameters(), gaussian_process.kernels.ConstantKernel.is_stationary(), gaussian_process.kernels.ConstantKernel.n_dims(), gaussian_process.kernels.ConstantKernel.requires_vector_input(), gaussian_process.kernels.ConstantKernel.set_params(), gaussian_process.kernels.ConstantKernel.theta(), gaussian_process.kernels.DotProduct.__call__(), gaussian_process.kernels.DotProduct.bounds(), gaussian_process.kernels.DotProduct.clone_with_theta(), gaussian_process.kernels.DotProduct.diag(), gaussian_process.kernels.DotProduct.get_params(), gaussian_process.kernels.DotProduct.hyperparameters(), gaussian_process.kernels.DotProduct.is_stationary(), gaussian_process.kernels.DotProduct.n_dims(), gaussian_process.kernels.DotProduct.requires_vector_input(), gaussian_process.kernels.DotProduct.set_params(), gaussian_process.kernels.DotProduct.theta(), gaussian_process.kernels.ExpSineSquared(), gaussian_process.kernels.ExpSineSquared.__call__(), gaussian_process.kernels.ExpSineSquared.bounds(), gaussian_process.kernels.ExpSineSquared.clone_with_theta(), gaussian_process.kernels.ExpSineSquared.diag(), gaussian_process.kernels.ExpSineSquared.get_params(), gaussian_process.kernels.ExpSineSquared.hyperparameter_length_scale(), gaussian_process.kernels.ExpSineSquared.hyperparameters(), gaussian_process.kernels.ExpSineSquared.is_stationary(), gaussian_process.kernels.ExpSineSquared.n_dims(), gaussian_process.kernels.ExpSineSquared.requires_vector_input(), gaussian_process.kernels.ExpSineSquared.set_params(), gaussian_process.kernels.ExpSineSquared.theta(), gaussian_process.kernels.Exponentiation(), gaussian_process.kernels.Exponentiation.__call__(), gaussian_process.kernels.Exponentiation.bounds(), gaussian_process.kernels.Exponentiation.clone_with_theta(), gaussian_process.kernels.Exponentiation.diag(), gaussian_process.kernels.Exponentiation.get_params(), gaussian_process.kernels.Exponentiation.hyperparameters(), gaussian_process.kernels.Exponentiation.is_stationary(), gaussian_process.kernels.Exponentiation.n_dims(), gaussian_process.kernels.Exponentiation.requires_vector_input(), gaussian_process.kernels.Exponentiation.set_params(), gaussian_process.kernels.Exponentiation.theta(), gaussian_process.kernels.Hyperparameter(), gaussian_process.kernels.Hyperparameter.__call__(), gaussian_process.kernels.Hyperparameter.bounds, gaussian_process.kernels.Hyperparameter.count(), gaussian_process.kernels.Hyperparameter.fixed, gaussian_process.kernels.Hyperparameter.index(), gaussian_process.kernels.Hyperparameter.n_elements, gaussian_process.kernels.Hyperparameter.name, gaussian_process.kernels.Hyperparameter.value_type, gaussian_process.kernels.Kernel.__call__(), gaussian_process.kernels.Kernel.clone_with_theta(), gaussian_process.kernels.Kernel.get_params(), gaussian_process.kernels.Kernel.hyperparameters(), gaussian_process.kernels.Kernel.is_stationary(), gaussian_process.kernels.Kernel.requires_vector_input(), gaussian_process.kernels.Kernel.set_params(), gaussian_process.kernels.Matern.__call__(), gaussian_process.kernels.Matern.clone_with_theta(), gaussian_process.kernels.Matern.get_params(), gaussian_process.kernels.Matern.hyperparameters(), gaussian_process.kernels.Matern.is_stationary(), gaussian_process.kernels.Matern.requires_vector_input(), gaussian_process.kernels.Matern.set_params(), gaussian_process.kernels.PairwiseKernel(), gaussian_process.kernels.PairwiseKernel.__call__(), gaussian_process.kernels.PairwiseKernel.bounds(), gaussian_process.kernels.PairwiseKernel.clone_with_theta(), gaussian_process.kernels.PairwiseKernel.diag(), gaussian_process.kernels.PairwiseKernel.get_params(), gaussian_process.kernels.PairwiseKernel.hyperparameters(), gaussian_process.kernels.PairwiseKernel.is_stationary(), gaussian_process.kernels.PairwiseKernel.n_dims(), gaussian_process.kernels.PairwiseKernel.requires_vector_input(), gaussian_process.kernels.PairwiseKernel.set_params(), gaussian_process.kernels.PairwiseKernel.theta(), gaussian_process.kernels.Product.__call__(), gaussian_process.kernels.Product.bounds(), gaussian_process.kernels.Product.clone_with_theta(), gaussian_process.kernels.Product.get_params(), gaussian_process.kernels.Product.hyperparameters(), gaussian_process.kernels.Product.is_stationary(), gaussian_process.kernels.Product.n_dims(), gaussian_process.kernels.Product.requires_vector_input(), gaussian_process.kernels.Product.set_params(), gaussian_process.kernels.RBF.clone_with_theta(), gaussian_process.kernels.RBF.get_params(), gaussian_process.kernels.RBF.hyperparameters(), gaussian_process.kernels.RBF.is_stationary(), gaussian_process.kernels.RBF.requires_vector_input(), gaussian_process.kernels.RBF.set_params(), gaussian_process.kernels.RationalQuadratic, gaussian_process.kernels.RationalQuadratic(), gaussian_process.kernels.RationalQuadratic.__call__(), gaussian_process.kernels.RationalQuadratic.bounds(), gaussian_process.kernels.RationalQuadratic.clone_with_theta(), gaussian_process.kernels.RationalQuadratic.diag(), gaussian_process.kernels.RationalQuadratic.get_params(), gaussian_process.kernels.RationalQuadratic.hyperparameters(), gaussian_process.kernels.RationalQuadratic.is_stationary(), gaussian_process.kernels.RationalQuadratic.n_dims(), gaussian_process.kernels.RationalQuadratic.requires_vector_input(), gaussian_process.kernels.RationalQuadratic.set_params(), gaussian_process.kernels.RationalQuadratic.theta(), gaussian_process.kernels.Sum.clone_with_theta(), gaussian_process.kernels.Sum.get_params(), gaussian_process.kernels.Sum.hyperparameters(), gaussian_process.kernels.Sum.is_stationary(), gaussian_process.kernels.Sum.requires_vector_input(), gaussian_process.kernels.Sum.set_params(), gaussian_process.kernels.WhiteKernel.__call__(), gaussian_process.kernels.WhiteKernel.bounds(), gaussian_process.kernels.WhiteKernel.clone_with_theta(), gaussian_process.kernels.WhiteKernel.diag(), gaussian_process.kernels.WhiteKernel.get_params(), gaussian_process.kernels.WhiteKernel.hyperparameters(), gaussian_process.kernels.WhiteKernel.is_stationary(), gaussian_process.kernels.WhiteKernel.n_dims(), gaussian_process.kernels.WhiteKernel.requires_vector_input(), gaussian_process.kernels.WhiteKernel.set_params(), gaussian_process.kernels.WhiteKernel.theta(), inspection.PartialDependenceDisplay.plot(), sklearn.inspection.permutation_importance(), sklearn.inspection.plot_partial_dependence(), isotonic.IsotonicRegression.fit_transform(), kernel_approximation.AdditiveChi2Sampler(), kernel_approximation.AdditiveChi2Sampler.fit(), kernel_approximation.AdditiveChi2Sampler.fit_transform(), kernel_approximation.AdditiveChi2Sampler.get_params(), kernel_approximation.AdditiveChi2Sampler.set_params(), kernel_approximation.AdditiveChi2Sampler.transform(), kernel_approximation.Nystroem.fit_transform(), kernel_approximation.Nystroem.get_params(), kernel_approximation.Nystroem.set_params(), kernel_approximation.Nystroem.transform(), kernel_approximation.PolynomialCountSketch, kernel_approximation.PolynomialCountSketch(), kernel_approximation.PolynomialCountSketch.fit(), kernel_approximation.PolynomialCountSketch.fit_transform(), kernel_approximation.PolynomialCountSketch.get_params(), kernel_approximation.PolynomialCountSketch.set_params(), kernel_approximation.PolynomialCountSketch.transform(), kernel_approximation.RBFSampler.fit_transform(), kernel_approximation.RBFSampler.get_params(), kernel_approximation.RBFSampler.set_params(), kernel_approximation.RBFSampler.transform(), kernel_approximation.SkewedChi2Sampler.fit(), kernel_approximation.SkewedChi2Sampler.fit_transform(), kernel_approximation.SkewedChi2Sampler.get_params(), kernel_approximation.SkewedChi2Sampler.set_params(), kernel_approximation.SkewedChi2Sampler.transform(), linear_model.LinearRegression.get_params(), linear_model.LinearRegression.set_params(), linear_model.LogisticRegression.decision_function(), linear_model.LogisticRegression.densify(), linear_model.LogisticRegression.get_params(), linear_model.LogisticRegression.predict(), linear_model.LogisticRegression.predict_log_proba(), linear_model.LogisticRegression.predict_proba(), linear_model.LogisticRegression.set_params(), linear_model.LogisticRegression.sparsify(), linear_model.LogisticRegressionCV.decision_function(), linear_model.LogisticRegressionCV.densify(), linear_model.LogisticRegressionCV.get_params(), linear_model.LogisticRegressionCV.predict(), linear_model.LogisticRegressionCV.predict_log_proba(), linear_model.LogisticRegressionCV.predict_proba(), linear_model.LogisticRegressionCV.score(), linear_model.LogisticRegressionCV.set_params(), linear_model.LogisticRegressionCV.sparsify(), linear_model.MultiTaskElasticNet.get_params(), linear_model.MultiTaskElasticNet.predict(), linear_model.MultiTaskElasticNet.set_params(), linear_model.MultiTaskElasticNet.sparse_coef_(), linear_model.MultiTaskElasticNetCV.get_params(), linear_model.MultiTaskElasticNetCV.path(), linear_model.MultiTaskElasticNetCV.predict(), linear_model.MultiTaskElasticNetCV.score(), linear_model.MultiTaskElasticNetCV.set_params(), linear_model.MultiTaskLasso.sparse_coef_(), linear_model.MultiTaskLassoCV.get_params(), linear_model.MultiTaskLassoCV.set_params(), linear_model.OrthogonalMatchingPursuit.fit(), linear_model.OrthogonalMatchingPursuit.get_params(), linear_model.OrthogonalMatchingPursuit.predict(), linear_model.OrthogonalMatchingPursuit.score(), linear_model.OrthogonalMatchingPursuit.set_params(), linear_model.OrthogonalMatchingPursuitCV(), linear_model.OrthogonalMatchingPursuitCV.fit(), linear_model.OrthogonalMatchingPursuitCV.get_params(), linear_model.OrthogonalMatchingPursuitCV.predict(), linear_model.OrthogonalMatchingPursuitCV.score(), linear_model.OrthogonalMatchingPursuitCV.set_params(), linear_model.PassiveAggressiveClassifier(), linear_model.PassiveAggressiveClassifier.decision_function(), linear_model.PassiveAggressiveClassifier.densify(), linear_model.PassiveAggressiveClassifier.fit(), linear_model.PassiveAggressiveClassifier.get_params(), linear_model.PassiveAggressiveClassifier.partial_fit(), linear_model.PassiveAggressiveClassifier.predict(), linear_model.PassiveAggressiveClassifier.score(), linear_model.PassiveAggressiveClassifier.set_params(), linear_model.PassiveAggressiveClassifier.sparsify(), linear_model.PassiveAggressiveRegressor(), linear_model.Perceptron.decision_function(), linear_model.PoissonRegressor.get_params(), linear_model.PoissonRegressor.set_params(), linear_model.RANSACRegressor.get_params(), linear_model.RANSACRegressor.set_params(), linear_model.RidgeClassifier.decision_function(), linear_model.RidgeClassifier.get_params(), linear_model.RidgeClassifier.set_params(), linear_model.RidgeClassifierCV.decision_function(), linear_model.RidgeClassifierCV.get_params(), linear_model.RidgeClassifierCV.set_params(), linear_model.SGDClassifier.decision_function(), linear_model.SGDClassifier.predict_log_proba(), linear_model.SGDClassifier.predict_proba(), linear_model.TheilSenRegressor.get_params(), linear_model.TheilSenRegressor.set_params(), linear_model.TweedieRegressor.get_params(), linear_model.TweedieRegressor.set_params(), sklearn.linear_model.PassiveAggressiveRegressor(), sklearn.linear_model.orthogonal_mp_gram(), manifold.LocallyLinearEmbedding.fit_transform(), manifold.LocallyLinearEmbedding.get_params(), manifold.LocallyLinearEmbedding.set_params(), manifold.LocallyLinearEmbedding.transform(), manifold.SpectralEmbedding.fit_transform(), sklearn.manifold.locally_linear_embedding(), metrics.homogeneity_completeness_v_measure(), metrics.label_ranking_average_precision_score(), metrics.precision_recall_fscore_support(), sklearn.metrics.adjusted_mutual_info_score(), sklearn.metrics.average_precision_score(), sklearn.metrics.balanced_accuracy_score(), sklearn.metrics.calinski_harabasz_score(), sklearn.metrics.explained_variance_score(), sklearn.metrics.homogeneity_completeness_v_measure(), sklearn.metrics.label_ranking_average_precision_score(), sklearn.metrics.mean_absolute_percentage_error(), sklearn.metrics.multilabel_confusion_matrix(), sklearn.metrics.normalized_mutual_info_score(), sklearn.metrics.pairwise_distances_argmin(), sklearn.metrics.pairwise_distances_argmin_min(), sklearn.metrics.pairwise_distances_chunked(), sklearn.metrics.plot_precision_recall_curve(), sklearn.metrics.precision_recall_fscore_support(), sklearn.metrics.cluster.contingency_matrix(), sklearn.metrics.cluster.pair_confusion_matrix(), metrics.pairwise.nan_euclidean_distances(), metrics.pairwise.paired_cosine_distances(), metrics.pairwise.paired_euclidean_distances(), metrics.pairwise.paired_manhattan_distances(), sklearn.metrics.pairwise.additive_chi2_kernel(), sklearn.metrics.pairwise.cosine_distances(), sklearn.metrics.pairwise.cosine_similarity(), sklearn.metrics.pairwise.distance_metrics(), sklearn.metrics.pairwise.euclidean_distances(), sklearn.metrics.pairwise.haversine_distances(), sklearn.metrics.pairwise.kernel_metrics(), sklearn.metrics.pairwise.laplacian_kernel(), sklearn.metrics.pairwise.manhattan_distances(), sklearn.metrics.pairwise.nan_euclidean_distances(), sklearn.metrics.pairwise.paired_cosine_distances(), sklearn.metrics.pairwise.paired_distances(), sklearn.metrics.pairwise.paired_euclidean_distances(), sklearn.metrics.pairwise.paired_manhattan_distances(), sklearn.metrics.pairwise.pairwise_kernels(), sklearn.metrics.pairwise.polynomial_kernel(), sklearn.metrics.pairwise.sigmoid_kernel(), mixture.BayesianGaussianMixture.fit_predict(), mixture.BayesianGaussianMixture.get_params(), mixture.BayesianGaussianMixture.predict(), mixture.BayesianGaussianMixture.predict_proba(), mixture.BayesianGaussianMixture.score_samples(), mixture.BayesianGaussianMixture.set_params(), model_selection.GridSearchCV.decision_function(), model_selection.GridSearchCV.get_params(), model_selection.GridSearchCV.inverse_transform(), model_selection.GridSearchCV.predict_log_proba(), model_selection.GridSearchCV.predict_proba(), model_selection.GridSearchCV.score_samples(), model_selection.GridSearchCV.set_params(), model_selection.GroupKFold.get_n_splits(), model_selection.GroupShuffleSplit.get_n_splits(), model_selection.GroupShuffleSplit.split(), model_selection.HalvingGridSearchCV.decision_function(), model_selection.HalvingGridSearchCV.fit(), model_selection.HalvingGridSearchCV.get_params(), model_selection.HalvingGridSearchCV.inverse_transform(), model_selection.HalvingGridSearchCV.predict(), model_selection.HalvingGridSearchCV.predict_log_proba(), model_selection.HalvingGridSearchCV.predict_proba(), model_selection.HalvingGridSearchCV.score(), model_selection.HalvingGridSearchCV.score_samples(), model_selection.HalvingGridSearchCV.set_params(), model_selection.HalvingGridSearchCV.transform(), model_selection.HalvingRandomSearchCV.decision_function(), model_selection.HalvingRandomSearchCV.fit(), model_selection.HalvingRandomSearchCV.get_params(), model_selection.HalvingRandomSearchCV.inverse_transform(), model_selection.HalvingRandomSearchCV.predict(), model_selection.HalvingRandomSearchCV.predict_log_proba(), model_selection.HalvingRandomSearchCV.predict_proba(), model_selection.HalvingRandomSearchCV.score(), model_selection.HalvingRandomSearchCV.score_samples(), model_selection.HalvingRandomSearchCV.set_params(), model_selection.HalvingRandomSearchCV.transform(), model_selection.LeaveOneGroupOut.get_n_splits(), model_selection.LeaveOneOut.get_n_splits(), model_selection.LeavePGroupsOut.get_n_splits(), model_selection.PredefinedSplit.get_n_splits(), model_selection.RandomizedSearchCV.decision_function(), model_selection.RandomizedSearchCV.get_params(), model_selection.RandomizedSearchCV.inverse_transform(), model_selection.RandomizedSearchCV.predict(), model_selection.RandomizedSearchCV.predict_log_proba(), model_selection.RandomizedSearchCV.predict_proba(), model_selection.RandomizedSearchCV.score(), model_selection.RandomizedSearchCV.score_samples(), model_selection.RandomizedSearchCV.set_params(), model_selection.RandomizedSearchCV.transform(), model_selection.RepeatedKFold.get_n_splits(), model_selection.RepeatedStratifiedKFold(), model_selection.RepeatedStratifiedKFold.get_n_splits(), model_selection.RepeatedStratifiedKFold.split(), model_selection.ShuffleSplit.get_n_splits(), model_selection.StratifiedKFold.get_n_splits(), model_selection.StratifiedShuffleSplit.get_n_splits(), model_selection.StratifiedShuffleSplit.split(), model_selection.TimeSeriesSplit.get_n_splits(), sklearn.model_selection.cross_val_predict(), sklearn.model_selection.cross_val_score(), sklearn.model_selection.permutation_test_score(), sklearn.model_selection.train_test_split(), sklearn.model_selection.validation_curve(), multioutput.ClassifierChain.decision_function(), multioutput.ClassifierChain.predict_proba(), multioutput.MultiOutputClassifier.get_params(), multioutput.MultiOutputClassifier.partial_fit(), multioutput.MultiOutputClassifier.predict(), multioutput.MultiOutputClassifier.predict_proba(), multioutput.MultiOutputClassifier.score(), multioutput.MultiOutputClassifier.set_params(), multioutput.MultiOutputRegressor.get_params(), multioutput.MultiOutputRegressor.partial_fit(), multioutput.MultiOutputRegressor.predict(), multioutput.MultiOutputRegressor.set_params(), naive_bayes.BernoulliNB.predict_log_proba(), naive_bayes.CategoricalNB.predict_log_proba(), naive_bayes.CategoricalNB.predict_proba(), naive_bayes.ComplementNB.predict_log_proba(), naive_bayes.GaussianNB.predict_log_proba(), naive_bayes.MultinomialNB.predict_log_proba(), naive_bayes.MultinomialNB.predict_proba(), neighbors.BallTree.two_point_correlation(), neighbors.KNeighborsClassifier.get_params(), neighbors.KNeighborsClassifier.kneighbors(), neighbors.KNeighborsClassifier.kneighbors_graph(), neighbors.KNeighborsClassifier.predict_proba(), neighbors.KNeighborsClassifier.set_params(), neighbors.KNeighborsRegressor.get_params(), neighbors.KNeighborsRegressor.kneighbors(), neighbors.KNeighborsRegressor.kneighbors_graph(), neighbors.KNeighborsRegressor.set_params(), neighbors.KNeighborsTransformer.fit_transform(), neighbors.KNeighborsTransformer.get_params(), neighbors.KNeighborsTransformer.kneighbors(), neighbors.KNeighborsTransformer.kneighbors_graph(), neighbors.KNeighborsTransformer.set_params(), neighbors.KNeighborsTransformer.transform(), neighbors.LocalOutlierFactor.decision_function(), neighbors.LocalOutlierFactor.fit_predict(), neighbors.LocalOutlierFactor.get_params(), neighbors.LocalOutlierFactor.kneighbors(), neighbors.LocalOutlierFactor.kneighbors_graph(), neighbors.LocalOutlierFactor.score_samples(), neighbors.LocalOutlierFactor.set_params(), neighbors.NearestNeighbors.kneighbors_graph(), neighbors.NearestNeighbors.radius_neighbors(), neighbors.NearestNeighbors.radius_neighbors_graph(), neighbors.NeighborhoodComponentsAnalysis(), neighbors.NeighborhoodComponentsAnalysis.fit(), neighbors.NeighborhoodComponentsAnalysis.fit_transform(), neighbors.NeighborhoodComponentsAnalysis.get_params(), neighbors.NeighborhoodComponentsAnalysis.set_params(), neighbors.NeighborhoodComponentsAnalysis.transform(), neighbors.RadiusNeighborsClassifier.fit(), neighbors.RadiusNeighborsClassifier.get_params(), neighbors.RadiusNeighborsClassifier.predict(), neighbors.RadiusNeighborsClassifier.predict_proba(), neighbors.RadiusNeighborsClassifier.radius_neighbors(), neighbors.RadiusNeighborsClassifier.radius_neighbors_graph(), neighbors.RadiusNeighborsClassifier.score(), neighbors.RadiusNeighborsClassifier.set_params(), neighbors.RadiusNeighborsRegressor.get_params(), neighbors.RadiusNeighborsRegressor.predict(), neighbors.RadiusNeighborsRegressor.radius_neighbors(), neighbors.RadiusNeighborsRegressor.radius_neighbors_graph(), neighbors.RadiusNeighborsRegressor.score(), neighbors.RadiusNeighborsRegressor.set_params(), neighbors.RadiusNeighborsTransformer.fit(), neighbors.RadiusNeighborsTransformer.fit_transform(), neighbors.RadiusNeighborsTransformer.get_params(), neighbors.RadiusNeighborsTransformer.radius_neighbors(), neighbors.RadiusNeighborsTransformer.radius_neighbors_graph(), neighbors.RadiusNeighborsTransformer.set_params(), neighbors.RadiusNeighborsTransformer.transform(), sklearn.neighbors.radius_neighbors_graph(), neural_network.BernoulliRBM.fit_transform(), neural_network.BernoulliRBM.partial_fit(), neural_network.BernoulliRBM.score_samples(), neural_network.MLPClassifier.get_params(), neural_network.MLPClassifier.partial_fit(), neural_network.MLPClassifier.predict_log_proba(), neural_network.MLPClassifier.predict_proba(), neural_network.MLPClassifier.set_params(), neural_network.MLPRegressor.partial_fit(), pipeline.FeatureUnion.get_feature_names(), preprocessing.FunctionTransformer.fit_transform(), preprocessing.FunctionTransformer.get_params(), preprocessing.FunctionTransformer.inverse_transform(), preprocessing.FunctionTransformer.set_params(), preprocessing.FunctionTransformer.transform(), preprocessing.KBinsDiscretizer.fit_transform(), preprocessing.KBinsDiscretizer.get_params(), preprocessing.KBinsDiscretizer.inverse_transform(), preprocessing.KBinsDiscretizer.set_params(), preprocessing.KBinsDiscretizer.transform(), preprocessing.KernelCenterer.fit_transform(), preprocessing.KernelCenterer.get_params(), preprocessing.KernelCenterer.set_params(), preprocessing.LabelBinarizer.fit_transform(), preprocessing.LabelBinarizer.get_params(), preprocessing.LabelBinarizer.inverse_transform(), preprocessing.LabelBinarizer.set_params(), preprocessing.LabelEncoder.fit_transform(), preprocessing.LabelEncoder.inverse_transform(), preprocessing.MaxAbsScaler.fit_transform(), preprocessing.MaxAbsScaler.inverse_transform(), preprocessing.MinMaxScaler.fit_transform(), preprocessing.MinMaxScaler.inverse_transform(), preprocessing.MultiLabelBinarizer.fit_transform(), preprocessing.MultiLabelBinarizer.get_params(), preprocessing.MultiLabelBinarizer.inverse_transform(), preprocessing.MultiLabelBinarizer.set_params(), preprocessing.MultiLabelBinarizer.transform(), preprocessing.OneHotEncoder.fit_transform(), preprocessing.OneHotEncoder.get_feature_names(), preprocessing.OneHotEncoder.inverse_transform(), preprocessing.OrdinalEncoder.fit_transform(), preprocessing.OrdinalEncoder.get_params(), preprocessing.OrdinalEncoder.inverse_transform(), preprocessing.OrdinalEncoder.set_params(), preprocessing.PolynomialFeatures.fit_transform(), preprocessing.PolynomialFeatures.get_feature_names(), preprocessing.PolynomialFeatures.get_params(), preprocessing.PolynomialFeatures.set_params(), preprocessing.PolynomialFeatures.transform(), preprocessing.PowerTransformer.fit_transform(), preprocessing.PowerTransformer.get_params(), preprocessing.PowerTransformer.inverse_transform(), preprocessing.PowerTransformer.set_params(), preprocessing.PowerTransformer.transform(), preprocessing.QuantileTransformer.fit_transform(), preprocessing.QuantileTransformer.get_params(), preprocessing.QuantileTransformer.inverse_transform(), preprocessing.QuantileTransformer.set_params(), preprocessing.QuantileTransformer.transform(), preprocessing.RobustScaler.fit_transform(), preprocessing.RobustScaler.inverse_transform(), preprocessing.StandardScaler.fit_transform(), preprocessing.StandardScaler.get_params(), preprocessing.StandardScaler.inverse_transform(), preprocessing.StandardScaler.partial_fit(), preprocessing.StandardScaler.set_params(), sklearn.preprocessing.add_dummy_feature(), sklearn.preprocessing.quantile_transform(), random_projection.GaussianRandomProjection, random_projection.GaussianRandomProjection(), random_projection.GaussianRandomProjection.fit(), random_projection.GaussianRandomProjection.fit_transform(), random_projection.GaussianRandomProjection.get_params(), random_projection.GaussianRandomProjection.set_params(), random_projection.GaussianRandomProjection.transform(), random_projection.SparseRandomProjection(), random_projection.SparseRandomProjection.fit(), random_projection.SparseRandomProjection.fit_transform(), random_projection.SparseRandomProjection.get_params(), random_projection.SparseRandomProjection.set_params(), random_projection.SparseRandomProjection.transform(), random_projection.johnson_lindenstrauss_min_dim(), sklearn.random_projection.johnson_lindenstrauss_min_dim(), semi_supervised.LabelPropagation.get_params(), semi_supervised.LabelPropagation.predict(), semi_supervised.LabelPropagation.predict_proba(), semi_supervised.LabelPropagation.set_params(), semi_supervised.LabelSpreading.get_params(), semi_supervised.LabelSpreading.predict_proba(), semi_supervised.LabelSpreading.set_params(), semi_supervised.SelfTrainingClassifier.decision_function(), semi_supervised.SelfTrainingClassifier.fit(), semi_supervised.SelfTrainingClassifier.get_params(), semi_supervised.SelfTrainingClassifier.predict(), semi_supervised.SelfTrainingClassifier.predict_log_proba(), semi_supervised.SelfTrainingClassifier.predict_proba(), semi_supervised.SelfTrainingClassifier.score(), semi_supervised.SelfTrainingClassifier.set_params(), tree.DecisionTreeClassifier.cost_complexity_pruning_path(), tree.DecisionTreeClassifier.decision_path(), tree.DecisionTreeClassifier.feature_importances_(), tree.DecisionTreeClassifier.get_n_leaves(), tree.DecisionTreeClassifier.predict_log_proba(), tree.DecisionTreeClassifier.predict_proba(), tree.DecisionTreeRegressor.cost_complexity_pruning_path(), tree.DecisionTreeRegressor.decision_path(), tree.DecisionTreeRegressor.feature_importances_(), tree.DecisionTreeRegressor.get_n_leaves(), tree.ExtraTreeClassifier.cost_complexity_pruning_path(), tree.ExtraTreeClassifier.feature_importances_(), tree.ExtraTreeClassifier.predict_log_proba(), tree.ExtraTreeRegressor.cost_complexity_pruning_path(), tree.ExtraTreeRegressor.feature_importances_(), sklearn.utils.register_parallel_backend(), sklearn.utils.estimator_checks.check_estimator(), sklearn.utils.estimator_checks.parametrize_with_checks(), utils.estimator_checks.parametrize_with_checks(), sklearn.utils.extmath.randomized_range_finder(), sklearn.utils.graph.single_source_shortest_path_length(), utils.graph.single_source_shortest_path_length(), sklearn.utils.graph_shortest_path.graph_shortest_path(), utils.graph_shortest_path.graph_shortest_path(), sklearn.utils.metaestimators.if_delegate_has_method(), utils.metaestimators.if_delegate_has_method(), sklearn.utils.random.sample_without_replacement(), utils.random.sample_without_replacement(), sklearn.utils.sparsefuncs.incr_mean_variance_axis(), sklearn.utils.sparsefuncs.inplace_column_scale(), sklearn.utils.sparsefuncs.inplace_csr_column_scale(), sklearn.utils.sparsefuncs.inplace_row_scale(), sklearn.utils.sparsefuncs.inplace_swap_column(), sklearn.utils.sparsefuncs.inplace_swap_row(), sklearn.utils.sparsefuncs.mean_variance_axis(), utils.sparsefuncs.incr_mean_variance_axis(), utils.sparsefuncs.inplace_csr_column_scale(), sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(), sklearn.utils.sparsefuncs_fast.inplace_csr_row_normalize_l2(), utils.sparsefuncs_fast.inplace_csr_row_normalize_l1(), utils.sparsefuncs_fast.inplace_csr_row_normalize_l2(), sklearn.utils.validation.check_is_fitted(), sklearn.utils.validation.check_symmetric(), sklearn.utils.validation.has_fit_parameter().
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