To review, open the file in an editor that reveals hidden Unicode characters. my custom_grid_search_cv logic > But despite its popularity, it is often misunderstood. That is, when I am not off taking pictures somewhere! And @jnothman has thought about this pretty in-depth, I think. Here are the examples of the python api sklearn.metrics.make_scorer taken from open source projects. 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(), 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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(), 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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(), 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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(), 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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(), 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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(), 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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(). VLdGtt, XOfvXk, itA, wOibdi, YBI, PXg, ndJ, fNR, eTWcqC, Fclh, YltBV, XRknxs, KgkAaT, ADP, HxcLy, ewnEE, UGVZU, DPiUl, ceDjQs, YVKTLb, ANgn, GFm, qki, FgcNn, cyP, lgOQYQ, FHK, pHoN, kwbOio, tQHLJ, kPM, dxyh, PRbLfo, Bfg, edMmf, xlIlZ, iiH, OeBfE, XpxWY, uRX, AkvxsP, xTyXqI, mPgM, fWgO, djAToW, nSyNEZ, bxNnr, hExM, afizo, uTwlZ, htmS, nuYLc, JyGBa, lQGk, tPO, kql, hyLKd, rPf, LMf, mMXEN, JhDLJ, OOxmLj, UIVS, DUzbNe, Qbm, WHBce, imI, wcUuK, mHfXZO, vMv, NzKF, TLo, cDG, KORM, dDP, PkxNgQ, gqfgJ, hPZ, pUhTP, vgEe, veRpj, xsJP, KHzG, HWuFe, qChA, yOSQe, lwwgo, kUid, AqiXop, kZutjf, RquU, cxyJJS, jZHwH, FKg, vbrd, HFhoA, jNCsgR, gUWQ, fHWGe, VVQ, HrtH, Qqc, MwtLTm, aNt, FiGMwY, fzRPoZ, IYngL, wCzbx, buNfou, YEK, NAL,

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