In classification, we are a lot happier using a loss function and a score functoin that are different. I also believe I am in the minority in this view that recall is a pathelogical score, so it is probably best you don't repeat this point of view while on an interview.). An initial, close to 0 decision threshold is chosen. How to create/customize your own scorer function in scikit-learn with Unsupervised dimensionality reduction, 6.8. This only works for binary classification using estimators that have either a decision_function or predict_proba method. A classification is a form of data analysis that extracts models describing important data classes. So indeed that could be seen as a limitation of make_scorer but it's not really the core issue. We can find a list of build-in scores with the following code: This lists the 35 (at the time of writing) different scores that sklearn already recognizes. sklearn.metrics.get_scorer(scoring) [source], sklearn.metrics.auc(x, y, reorder=False) [source] This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. Can you repurpose a $$ \text{MAPE} = \frac{1}{n}\sum_{i=1}^n |\text{% error in }y_{\text{predict, i}}| = \frac{1}{n}\sum_i \frac{|y_{\text{true, i}} - y_{\text{predict, i}}|}{|y_{\text{true, i}}|} $$. When looking at the documentation for Ridge and Lasso, you won't find a scoring parameter. Examples >>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer (fbeta_score, beta=2) >>> ftwo_scorer make_scorer (fbeta_score, beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV (LinearSVC (), param_grid= {'C': [1, 10]}, . That is, when I am not off taking pictures somewhere! Other examples are. The following are 14 code examples of sklearn.metrics.get_scorer () . There is no notion of training and test set in your code. # Here are some parameters to search over. # Doesn't this minimize mean absolute error? shufflebool, default=True Shuffle the samples and the features. Whether score_func requires predict_proba to get probability estimates out of a classifier. What is the motivation of using cross-validation in this setting? Score function (or loss function) with signature score_func(y, y_pred, **kwargs). You could probably hack the CV splitter to use the full data both as training and test set to sort-of get around this, but it's a bit ugly. Instead, for each combination of hyperparameters we train a random forest in the usual way (minimizing the entropy or Gini score). scalefloat, ndarray of shape (n_features,) or None, default=1.0 Multiply features by the specified value. Here are just a few of the attributes of logistic regression that make it incredibly popular: it's fast, it's highly interpretable, it doesn't require input features to be scaled, it doesn't require any tuning, it's easy to regularize, and it outputs well-calibrated predicted probabilities. Valid metrics for pairwise_kernels This function simply returns the valid pairwise distance me, sklearn.metrics.roc_curve(y_true, y_score, pos_label=None, sample_weight=None, drop_intermediate=True) [source] at Keras) or writing your own estimator. The classification metrics is a process that requires probability evaluation of the positive class. # This was our original way of using cross-validation using MAE: # Note we would use the scoring parameter in GridSearchCV or others, # This is equivalent, using our custom scorer, # Ignore for demo -- in some sense an unsolvable. sklearn make_scorer example For quantile loss, or Mean Absolute Percent Error (MAPE) you either have to use a different package such as statsmodels or roll-your-own. scoring=ftwo_scorer) How to use make_scorer Custom scoring function in sklearn I thinks we cannot use make_scorer() with a GridSearchCV for a clustering task. compare_scores() what is the initialization score, # Each possible combination of parameters, #opt = base_opt # uncomment this if you don't want the grid search. Additional parameters to be passed to score_func. Interested in Algorithms, Games, Books, Music, and Martial Arts. After running the above code we get the following output in which we can see a loss function is printed on the screen. But despite its popularity, it is often misunderstood. Creating a bag-of-words in scikit-learn feature importance plot using lasso regression from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X, y = make_classification (n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=0, shuffle=False) clf = RandomForestClassifier (max_d You can rate examples to help us improve the quality of examples. Sklearn's usage "uses up" a perfectly good term "loss" instead of just talking about a score we are trying to minimize. If I would not optimize against recall directly -- and I shouldn't -- it is because it is pathelogical, and so I shouldn't use it to select between my models either. The first step is to see if we need to, or if it is already implemented for us. Whether score_func requires predict_proba to get probability estimates out of a classifier. Read: Scikit learn Hierarchical Clustering. Earn . scoring : str or callable, default=None. I've tried all clustering metrics from sklearn.metrics. Model Evaluation & Scoring Matrices. Python make_scorer - 30 examples found. It takes a score function, such as accuracy_score , mean_squared_error, adjusted_rand_index or average_precision and returns a callable that scores an estimator's output. After running the above code, we get the following output in which we can see that the report support score is printed on the screen. AttributeError: 'OPTICS' object has no attribute 'predict'. If you use MSE as your loss, and MAE as your scoring, you are unlikely to find the best answer. Score function (or loss function) with signature score_func(y, y_pred, **kwargs). Same issue holds true for DBSCAN. The simple approaches are. We will never be able to have Ridge or Lasso support even a simple error such as Mean Absolute Error. This isn't fundamentally any different from what is happening when we find coefficients using MSE and then select the model with the lowest MAE, instead of using MAE as both the loss and the scoring. The signature of the call is (estimator, X, y) where estimator is the model to be evaluated, X is the data and y is the ground truth labeling (or None in the case of unsupervised models). the parameters grid grid_search_params) for a clustering estimator, with or without labels (in my case I have labels). Scikit Learn Classification Tutorial - Python Guides By voting up you can indicate which examples are most useful and appropriate. Multiclass and multioutput algorithms, 1.2. In this section, we will learn how scikit learn classification metrics works in python. After running the above code we get the following output in which we can see that the classification report is printed on the screen. TypeError: _score() missing 1 required positional argument: 'y_true'. It takes a score function, such as accuracy_score , mean_squared_error , adjusted_rand_score or average_precision_score and returns a callable that scores an estimator's output. Valid metrics for pairwise_distances. In this section, we will learn how scikit learn classification report support works in python. I have a machine learning model where unphysical values are modified before scoring. There are two different things happening: So we only apply the scoring parameter when choosing between models, not when fitting the individual models themselves. #4301. For this particular loss, you can use SGDRegressor to minimize MAE. allow_none : bool, default=False. Python Examples of sklearn.metrics.make_scorer - ProgramCreek.com sklearn.metrics.make_scorer sklearn.metrics.make_scorer (score_func, *, greater_is_better=True, needs_proba=False, needs_threshold=False, **kwargs) [source] Make a scorer from a performance metric or loss function. It isn't you that is confused! A classification tree is a supervised learning method. Note that scaling happens after shifting. discord level rewards 157 E. New England Ave #202, Winter Park, FL 32789 : , : The Problem You have more than one model that you want to score. If the score you want isn't on that list, then you can build a custom scorer. gridsearchcv scoring options Examples >>> from sklearn.metrics import fbeta_score, make_scorer >>> ftwo_scorer = make_scorer (fbeta_score, beta=2) >>> ftwo_scorer make_scorer (fbeta_score, beta=2) >>> from sklearn.model_selection import GridSearchCV >>> from sklearn.svm import LinearSVC >>> grid = GridSearchCV (LinearSVC (), param_grid= {'C': [1, 10]}, . The classification metrics is a process that requires probability evaluation of the positive class. Now in case we don't have the labels, we could have something like: I think we should either support this case, or raise a more informative error. Already on GitHub? The following are 30 code examples of sklearn.metrics.make_scorer () . In the latter case, the scorer object will sign-flip the outcome of the score_func. scikit-learn - sklearn.metrics.make_scorer Make scorer from performance This factory function wraps scoring functions for use in GridSearchCV and cross_val_score . Compute Area Under the Curve (AUC) using the trapezoidal rule This is a general function, given, sklearn.metrics.pairwise.distance_metrics(), sklearn.metrics.pairwise.distance_metrics() [source] If True, for binary y_true, the score function is supposed to accept a 1D y_pred (i.e., probability of the positive class or the decision function, shape (n_samples,)). The make_scorer function allows us to specify directly whether we should maximize or minimize. scikit-learn/_scorer.py at main scikit-learn/scikit-learn GitHub In the following code, we will import gaussianProcessClassifier from sklearn.gaussian_process also import matplotlib.pyplot as plot by which we plot the probability classes. The function aims to create a model from which a target variable is predicted. sklearn.metrics.make_scorer scikit-learn 0.23.1 documentation Python is one of the most popular languages in the United States of America. In the standard implementation, it is assumed that the a higher score is better, which is why we see the functions we want to minimize appear in the negative form, such as neg_mean_absolute_error: minimizing the mean absolute error is the same as maximizing the negative of the mean absolute error. It is possible to get 100% recall by simply predicting everyone has the disease. Modifying an estimator in scikit-learn for use with CV Motivation: Search in the parameter space to find the best parameters choice for optics (or dbscan) model. Here is the list of examples that we have covered. Moreover, we will cover these topics. Scikit learn Classification Report Support, module matplotlib has no attribute artist, Scikit learn classification report support. Let's implement a new score, mean absolute percentage error (MAPE), that isn't predefined in sklearn. we would rather flag a healthy person eroneously than miss a sick person). sklearn.metrics.make_scorer scikit-learn 1.1.3 documentation In the following code, we will import cross_val_score from sklearn.model_selection by which we can calculate the cross value score. For example, if you use Gaussian Naive Bayes, the scoring method is the mean accuracy on the given test data and labels. sklearn make_scorer example While it is clearly useful, function calls in Python are slow. Now if you replace it with KMeans: it works fine. While this is generally true, we are far more comfortable with the idea that loss and scoring being different in classification problems. Since predict is well-defined for kmeans. Also, take a look at some more articles on Scikit learn. Earn Free Access Learn More > Upload Documents For instance, if I use LASSO and get a vector of predicted values y, I will do something like y [y<0]=0 before evaluating the success of the model. Now that we understand the difference between a loss and a scorer, how do we implement a custom score? Saying "GridSearchCV should support clustering estimators as well." washington state sick leave law doctor's note Login Login Write a custom loss in Keras. Partial Dependence and Individual Conditional Expectation plots, 6.5. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Custom losses require looking outside sklearn (e.g. By clicking Sign up for GitHub, you agree to our terms of service and This gives a nice distinction between a loss (used when fitting) and a score (used when choosing between fit models). Scoring function to compute the LIFT metric, the ratio of correctly predicted positive examples and the actual positive examples in the test dataset. (I would put forward an opinion that because recall is a bad loss, it is also a bad scorer. The difference is a custom score is called once per model, while a custom loss would be called thousands of times per model. You might think that you could optimize for mean absolute error in the following way: Not really. This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. Python SCORERS Examples, sklearnmetrics.SCORERS Python Examples You can ask !. Pairwise metrics, Affinities and Kernels, Tutorial: A tutorial on statistical-learning for scientific data processing, Tutorial: An introduction to machine learning with scikit-learn, multiclass.OneVsOneClassifier.decision_function(), multiclass.OneVsOneClassifier.get_params(), multiclass.OneVsOneClassifier.partial_fit(), multiclass.OneVsOneClassifier.set_params(), multiclass.OneVsRestClassifier.decision_function(), multiclass.OneVsRestClassifier.get_params(), multiclass.OneVsRestClassifier.multilabel_(), multiclass.OneVsRestClassifier.partial_fit(), multiclass.OneVsRestClassifier.predict_proba(), multiclass.OneVsRestClassifier.set_params(), multiclass.OutputCodeClassifier.get_params(), multiclass.OutputCodeClassifier.predict(), multiclass.OutputCodeClassifier.set_params(), sklearn.utils.class_weight.compute_class_weight(), sklearn.utils.class_weight.compute_sample_weight(), utils.class_weight.compute_class_weight(), utils.class_weight.compute_sample_weight(), sklearn.utils.multiclass.type_of_target(), Example: A demo of K-Means clustering on the handwritten digits data, Example: A demo of structured Ward hierarchical clustering on an image of coins, Example: A demo of the Spectral Biclustering algorithm, Example: A demo of the Spectral Co-Clustering algorithm, Example: A demo of the mean-shift clustering algorithm, Example: Adjustment for chance in clustering performance evaluation, Example: Advanced Plotting With Partial Dependence, Example: Agglomerative clustering with and without structure, Example: Agglomerative clustering with different metrics, Example: An example of K-Means++ initialization, Example: Approximate nearest neighbors in TSNE, Example: Automatic Relevance Determination Regression, Example: Balance model complexity and cross-validated score, Example: Biclustering documents with the Spectral Co-clustering algorithm, Example: Blind source separation using FastICA, Example: Categorical Feature Support in Gradient Boosting, Example: Classification of text documents using sparse features, Example: Clustering text documents using k-means, Example: Color Quantization using K-Means, Example: Column Transformer with Heterogeneous Data Sources, Example: Column Transformer with Mixed Types, Example: Combine predictors using stacking, Example: Common pitfalls in interpretation of coefficients of linear models, Example: Compact estimator representations, Example: Compare BIRCH and MiniBatchKMeans, Example: Compare Stochastic learning strategies for MLPClassifier, Example: Compare cross decomposition methods, Example: Compare the effect of different scalers on data with outliers, Example: Comparing Nearest Neighbors with and without Neighborhood Components Analysis, Example: Comparing anomaly detection algorithms for outlier detection on toy datasets, Example: Comparing different clustering algorithms on toy datasets, Example: Comparing different hierarchical linkage methods on toy datasets, Example: Comparing random forests and the multi-output meta estimator, Example: Comparing randomized search and grid search for hyperparameter estimation, Example: Comparing various online solvers, Example: Comparison between grid search and successive halving, Example: Comparison of Calibration of Classifiers, Example: Comparison of F-test and mutual information, Example: Comparison of LDA and PCA 2D projection of Iris dataset, Example: Comparison of Manifold Learning methods, Example: Comparison of kernel ridge and Gaussian process regression, Example: Comparison of kernel ridge regression and SVR, Example: Comparison of the K-Means and MiniBatchKMeans clustering algorithms, Example: Concatenating multiple feature extraction methods, Example: Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture, Example: Cross-validation on Digits Dataset Exercise, Example: Cross-validation on diabetes Dataset Exercise, Example: Curve Fitting with Bayesian Ridge Regression, Example: Decision Tree Regression with AdaBoost, Example: Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset, Example: Demo of DBSCAN clustering algorithm, Example: Demo of OPTICS clustering algorithm, Example: Demo of affinity propagation clustering algorithm, Example: Demonstrating the different strategies of KBinsDiscretizer, Example: Demonstration of k-means assumptions, Example: Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV, Example: Density Estimation for a Gaussian mixture, Example: Dimensionality Reduction with Neighborhood Components Analysis, Example: Early stopping of Gradient Boosting, Example: Early stopping of Stochastic Gradient Descent, Example: Effect of transforming the targets in regression model, Example: Effect of varying threshold for self-training, Example: Empirical evaluation of the impact of k-means initialization, Example: Explicit feature map approximation for RBF kernels, Example: Face completion with a multi-output estimators, Example: Faces recognition example using eigenfaces and SVMs, Example: Factor Analysis to visualize patterns, Example: Feature agglomeration vs. univariate selection, Example: Feature importances with forests of trees, Example: Feature transformations with ensembles of trees, Example: FeatureHasher and DictVectorizer Comparison, Example: Gaussian Mixture Model Ellipsoids, Example: Gaussian Mixture Model Selection, Example: Gaussian Mixture Model Sine Curve, Example: Gaussian process classification on iris dataset. In the following code, we will import accuracy_score from sklearn.metrics that implement score, probability functions to calculate classification performance. So, in this tutorial, we discussed scikit learn classification and we have also covered different examples related to its implementation. TypeError: _score() missing 1 required positional argument: 'y_true', Even by using grid_search_cv.fit(data, labels) instead of grid_search_cv.fit(data), another exception rised: Btw, there is a lot of discussion here: vincent vineyards v ranch Search. This factory function wraps scoring functions for use in GridSearchCV and cross_val_score. bash echo variable with newlines. But tbh I think that's a very strange thing to do. A new threshold is chosen, and steps 3-4 are repeated. We can use LinearRegression, Ridge, or Lasso that optimize on finding the smallest MSE, and this matches the thing we want to optimize. You could do what you're doing in your code with GridSearchCV by using a custom splitter and custom scorer. ``scorer (estimator, X, y)``. sklearn make_scorer example - sumalim.com def training (matrix, Y, SVM): """ def training (matrix , Y , svm ): matrix: is the train data Y: is the labels in array . This sounds complicated, but let's build mean absolute error as a scorer to see how it would work. You can rate examples to help us improve the quality of examples. random_stateint, RandomState instance or None, default=None In the following code, we will import some libraries from which we can perform the classification task. I think that's an appropriate error message. child of yemaya characteristics; rotate youtube video while watching While common, MSE isn't necessarily the best error metric for your problem. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. 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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