API Reference

This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines on their uses. For reference on concepts repeated across the API, see Glossary of Common Terms and API Elements.

Object	Description
config_context	Context manager for global scikit-learn configuration. sklearn
get_config	Retrieve current values for configuration set by set_config. sklearn
set_config	Set global scikit-learn configuration. sklearn
show_versions	Print useful debugging information” sklearn
BaseEstimator	Base class for all estimators in scikit-learn. sklearn.base
BiclusterMixin	Mixin class for all bicluster estimators in scikit-learn. sklearn.base
ClassNamePrefixFeaturesOutMixin	Mixin class for transformers that generate their own names by prefixing. sklearn.base
ClassifierMixin	Mixin class for all classifiers in scikit-learn. sklearn.base
ClusterMixin	Mixin class for all cluster estimators in scikit-learn. sklearn.base
DensityMixin	Mixin class for all density estimators in scikit-learn. sklearn.base
MetaEstimatorMixin	Mixin class for all meta estimators in scikit-learn. sklearn.base
OneToOneFeatureMixin	Provides get_feature_names_out for simple transformers. sklearn.base
OutlierMixin	Mixin class for all outlier detection estimators in scikit-learn. sklearn.base
RegressorMixin	Mixin class for all regression estimators in scikit-learn. sklearn.base
TransformerMixin	Mixin class for all transformers in scikit-learn. sklearn.base
clone	Construct a new unfitted estimator with the same parameters. sklearn.base
is_classifier	Return True if the given estimator is (probably) a classifier. sklearn.base
is_clusterer	Return True if the given estimator is (probably) a clusterer. sklearn.base
is_regressor	Return True if the given estimator is (probably) a regressor. sklearn.base
CalibratedClassifierCV	Probability calibration with isotonic regression or logistic regression. sklearn.calibration
calibration_curve	Compute true and predicted probabilities for a calibration curve. sklearn.calibration
CalibrationDisplay	Calibration curve (also known as reliability diagram) visualization. sklearn.calibration
AffinityPropagation	Perform Affinity Propagation Clustering of data. sklearn.cluster
AgglomerativeClustering	Agglomerative Clustering. sklearn.cluster
Birch	Implements the BIRCH clustering algorithm. sklearn.cluster
BisectingKMeans	Bisecting K-Means clustering. sklearn.cluster
DBSCAN	Perform DBSCAN clustering from vector array or distance matrix. sklearn.cluster
FeatureAgglomeration	Agglomerate features. sklearn.cluster
HDBSCAN	Cluster data using hierarchical density-based clustering. sklearn.cluster
KMeans	K-Means clustering. sklearn.cluster
MeanShift	Mean shift clustering using a flat kernel. sklearn.cluster
MiniBatchKMeans	Mini-Batch K-Means clustering. sklearn.cluster
OPTICS	Estimate clustering structure from vector array. sklearn.cluster
SpectralBiclustering	Spectral biclustering (Kluger, 2003). sklearn.cluster
SpectralClustering	Apply clustering to a projection of the normalized Laplacian. sklearn.cluster
SpectralCoclustering	Spectral Co-Clustering algorithm (Dhillon, 2001). sklearn.cluster
affinity_propagation	Perform Affinity Propagation Clustering of data. sklearn.cluster
cluster_optics_dbscan	Perform DBSCAN extraction for an arbitrary epsilon. sklearn.cluster
cluster_optics_xi	Automatically extract clusters according to the Xi-steep method. sklearn.cluster
compute_optics_graph	Compute the OPTICS reachability graph. sklearn.cluster
dbscan	Perform DBSCAN clustering from vector array or distance matrix. sklearn.cluster
estimate_bandwidth	Estimate the bandwidth to use with the mean-shift algorithm. sklearn.cluster
k_means	Perform K-means clustering algorithm. sklearn.cluster
kmeans_plusplus	Init n_clusters seeds according to k-means++. sklearn.cluster
mean_shift	Perform mean shift clustering of data using a flat kernel. sklearn.cluster
spectral_clustering	Apply clustering to a projection of the normalized Laplacian. sklearn.cluster
ward_tree	Ward clustering based on a Feature matrix. sklearn.cluster
ColumnTransformer	Applies transformers to columns of an array or pandas DataFrame. sklearn.compose
TransformedTargetRegressor	Meta-estimator to regress on a transformed target. sklearn.compose
make_column_selector	Create a callable to select columns to be used with sklearn.compose
make_column_transformer	Construct a ColumnTransformer from the given transformers. sklearn.compose
EllipticEnvelope	An object for detecting outliers in a Gaussian distributed dataset. sklearn.covariance
EmpiricalCovariance	Maximum likelihood covariance estimator. sklearn.covariance
GraphicalLasso	Sparse inverse covariance estimation with an l1-penalized estimator. sklearn.covariance
GraphicalLassoCV	Sparse inverse covariance w/ cross-validated choice of the l1 penalty. sklearn.covariance
LedoitWolf	LedoitWolf Estimator. sklearn.covariance
MinCovDet	Minimum Covariance Determinant (MCD): robust estimator of covariance. sklearn.covariance
OAS	Oracle Approximating Shrinkage Estimator. sklearn.covariance
ShrunkCovariance	Covariance estimator with shrinkage. sklearn.covariance
empirical_covariance	Compute the Maximum likelihood covariance estimator. sklearn.covariance
graphical_lasso	L1-penalized covariance estimator. sklearn.covariance
ledoit_wolf	Estimate the shrunk Ledoit-Wolf covariance matrix. sklearn.covariance
ledoit_wolf_shrinkage	Estimate the shrunk Ledoit-Wolf covariance matrix. sklearn.covariance
oas	Estimate covariance with the Oracle Approximating Shrinkage. sklearn.covariance
shrunk_covariance	Calculate covariance matrices shrunk on the diagonal. sklearn.covariance
CCA	Canonical Correlation Analysis, also known as “Mode B” PLS. sklearn.cross_decomposition
PLSCanonical	Partial Least Squares transformer and regressor. sklearn.cross_decomposition
PLSRegression	PLS regression. sklearn.cross_decomposition
PLSSVD	Partial Least Square SVD. sklearn.cross_decomposition
clear_data_home	Delete all the content of the data home cache. sklearn.datasets
dump_svmlight_file	Dump the dataset in svmlight / libsvm file format. sklearn.datasets
fetch_20newsgroups	Load the filenames and data from the 20 newsgroups dataset (classification). sklearn.datasets
fetch_20newsgroups_vectorized	Load and vectorize the 20 newsgroups dataset (classification). sklearn.datasets
fetch_california_housing	Load the California housing dataset (regression). sklearn.datasets
fetch_covtype	Load the covertype dataset (classification). sklearn.datasets
fetch_file	Fetch a file from the web if not already present in the local folder. sklearn.datasets
fetch_kddcup99	Load the kddcup99 dataset (classification). sklearn.datasets
fetch_lfw_pairs	Load the Labeled Faces in the Wild (LFW) pairs dataset (classification). sklearn.datasets
fetch_lfw_people	Load the Labeled Faces in the Wild (LFW) people dataset (classification). sklearn.datasets
fetch_olivetti_faces	Load the Olivetti faces data-set from AT&T (classification). sklearn.datasets
fetch_openml	Fetch dataset from openml by name or dataset id. sklearn.datasets
fetch_rcv1	Load the RCV1 multilabel dataset (classification). sklearn.datasets
fetch_species_distributions	Loader for species distribution dataset from Phillips et. al. (2006). sklearn.datasets
get_data_home	Return the path of the scikit-learn data directory. sklearn.datasets
load_breast_cancer	Load and return the breast cancer wisconsin dataset (classification). sklearn.datasets
load_diabetes	Load and return the diabetes dataset (regression). sklearn.datasets
load_digits	Load and return the digits dataset (classification). sklearn.datasets
load_files	Load text files with categories as subfolder names. sklearn.datasets
load_iris	Load and return the iris dataset (classification). sklearn.datasets
load_linnerud	Load and return the physical exercise Linnerud dataset. sklearn.datasets
load_sample_image	Load the numpy array of a single sample image. sklearn.datasets
load_sample_images	Load sample images for image manipulation. sklearn.datasets
load_svmlight_file	Load datasets in the svmlight / libsvm format into sparse CSR matrix. sklearn.datasets
load_svmlight_files	Load dataset from multiple files in SVMlight format. sklearn.datasets
load_wine	Load and return the wine dataset (classification). sklearn.datasets
make_biclusters	Generate a constant block diagonal structure array for biclustering. sklearn.datasets
make_blobs	Generate isotropic Gaussian blobs for clustering. sklearn.datasets
make_checkerboard	Generate an array with block checkerboard structure for biclustering. sklearn.datasets
make_circles	Make a large circle containing a smaller circle in 2d. sklearn.datasets
make_classification	Generate a random n-class classification problem. sklearn.datasets
make_friedman1	Generate the “Friedman #1” regression problem. sklearn.datasets
make_friedman2	Generate the “Friedman #2” regression problem. sklearn.datasets
make_friedman3	Generate the “Friedman #3” regression problem. sklearn.datasets
make_gaussian_quantiles	Generate isotropic Gaussian and label samples by quantile. sklearn.datasets
make_hastie_10_2	Generate data for binary classification used in Hastie et al. 2009, Example 10.2. sklearn.datasets
make_low_rank_matrix	Generate a mostly low rank matrix with bell-shaped singular values. sklearn.datasets
make_moons	Make two interleaving half circles. sklearn.datasets
make_multilabel_classification	Generate a random multilabel classification problem. sklearn.datasets
make_regression	Generate a random regression problem. sklearn.datasets
make_s_curve	Generate an S curve dataset. sklearn.datasets
make_sparse_coded_signal	Generate a signal as a sparse combination of dictionary elements. sklearn.datasets
make_sparse_spd_matrix	Generate a sparse symmetric definite positive matrix. sklearn.datasets
make_sparse_uncorrelated	Generate a random regression problem with sparse uncorrelated design. sklearn.datasets
make_spd_matrix	Generate a random symmetric, positive-definite matrix. sklearn.datasets
make_swiss_roll	Generate a swiss roll dataset. sklearn.datasets
DictionaryLearning	Dictionary learning. sklearn.decomposition
FactorAnalysis	Factor Analysis (FA). sklearn.decomposition
FastICA	FastICA: a fast algorithm for Independent Component Analysis. sklearn.decomposition
IncrementalPCA	Incremental principal components analysis (IPCA). sklearn.decomposition
KernelPCA	Kernel Principal component analysis (KPCA). sklearn.decomposition
LatentDirichletAllocation	Latent Dirichlet Allocation with online variational Bayes algorithm. sklearn.decomposition
MiniBatchDictionaryLearning	Mini-batch dictionary learning. sklearn.decomposition
MiniBatchNMF	Mini-Batch Non-Negative Matrix Factorization (NMF). sklearn.decomposition
MiniBatchSparsePCA	Mini-batch Sparse Principal Components Analysis. sklearn.decomposition
NMF	Non-Negative Matrix Factorization (NMF). sklearn.decomposition
PCA	Principal component analysis (PCA). sklearn.decomposition
SparseCoder	Sparse coding. sklearn.decomposition
SparsePCA	Sparse Principal Components Analysis (SparsePCA). sklearn.decomposition
TruncatedSVD	Dimensionality reduction using truncated SVD (aka LSA). sklearn.decomposition
dict_learning	Solve a dictionary learning matrix factorization problem. sklearn.decomposition
dict_learning_online	Solve a dictionary learning matrix factorization problem online. sklearn.decomposition
fastica	Perform Fast Independent Component Analysis. sklearn.decomposition
non_negative_factorization	Compute Non-negative Matrix Factorization (NMF). sklearn.decomposition
sparse_encode	Sparse coding. sklearn.decomposition
LinearDiscriminantAnalysis	Linear Discriminant Analysis. sklearn.discriminant_analysis
QuadraticDiscriminantAnalysis	Quadratic Discriminant Analysis. sklearn.discriminant_analysis
DummyClassifier	DummyClassifier makes predictions that ignore the input features. sklearn.dummy
DummyRegressor	Regressor that makes predictions using simple rules. sklearn.dummy
AdaBoostClassifier	An AdaBoost classifier. sklearn.ensemble
AdaBoostRegressor	An AdaBoost regressor. sklearn.ensemble
BaggingClassifier	A Bagging classifier. sklearn.ensemble
BaggingRegressor	A Bagging regressor. sklearn.ensemble
ExtraTreesClassifier	An extra-trees classifier. sklearn.ensemble
ExtraTreesRegressor	An extra-trees regressor. sklearn.ensemble
GradientBoostingClassifier	Gradient Boosting for classification. sklearn.ensemble
GradientBoostingRegressor	Gradient Boosting for regression. sklearn.ensemble
HistGradientBoostingClassifier	Histogram-based Gradient Boosting Classification Tree. sklearn.ensemble
HistGradientBoostingRegressor	Histogram-based Gradient Boosting Regression Tree. sklearn.ensemble
IsolationForest	Isolation Forest Algorithm. sklearn.ensemble
RandomForestClassifier	A random forest classifier. sklearn.ensemble
RandomForestRegressor	A random forest regressor. sklearn.ensemble
RandomTreesEmbedding	An ensemble of totally random trees. sklearn.ensemble
StackingClassifier	Stack of estimators with a final classifier. sklearn.ensemble
StackingRegressor	Stack of estimators with a final regressor. sklearn.ensemble
VotingClassifier	Soft Voting/Majority Rule classifier for unfitted estimators. sklearn.ensemble
VotingRegressor	Prediction voting regressor for unfitted estimators. sklearn.ensemble
ConvergenceWarning	Custom warning to capture convergence problems sklearn.exceptions
DataConversionWarning	Warning used to notify implicit data conversions happening in the code. sklearn.exceptions
DataDimensionalityWarning	Custom warning to notify potential issues with data dimensionality. sklearn.exceptions
EfficiencyWarning	Warning used to notify the user of inefficient computation. sklearn.exceptions
FitFailedWarning	Warning class used if there is an error while fitting the estimator. sklearn.exceptions
InconsistentVersionWarning	Warning raised when an estimator is unpickled with a inconsistent version. sklearn.exceptions
NotFittedError	Exception class to raise if estimator is used before fitting. sklearn.exceptions
UndefinedMetricWarning	Warning used when the metric is invalid sklearn.exceptions
enable_halving_search_cv	Enables Successive Halving search-estimators sklearn.experimental
enable_iterative_imputer	Enables IterativeImputer sklearn.experimental
DictVectorizer	Transforms lists of feature-value mappings to vectors. sklearn.feature_extraction
FeatureHasher	Implements feature hashing, aka the hashing trick. sklearn.feature_extraction
PatchExtractor	Extracts patches from a collection of images. sklearn.feature_extraction.image
extract_patches_2d	Reshape a 2D image into a collection of patches. sklearn.feature_extraction.image
grid_to_graph	Graph of the pixel-to-pixel connections. sklearn.feature_extraction.image
img_to_graph	Graph of the pixel-to-pixel gradient connections. sklearn.feature_extraction.image
reconstruct_from_patches_2d	Reconstruct the image from all of its patches. sklearn.feature_extraction.image
CountVectorizer	Convert a collection of text documents to a matrix of token counts. sklearn.feature_extraction.text
HashingVectorizer	Convert a collection of text documents to a matrix of token occurrences. sklearn.feature_extraction.text
TfidfTransformer	Transform a count matrix to a normalized tf or tf-idf representation. sklearn.feature_extraction.text
TfidfVectorizer	Convert a collection of raw documents to a matrix of TF-IDF features. sklearn.feature_extraction.text
GenericUnivariateSelect	Univariate feature selector with configurable strategy. sklearn.feature_selection
RFE	Feature ranking with recursive feature elimination. sklearn.feature_selection
RFECV	Recursive feature elimination with cross-validation to select features. sklearn.feature_selection
SelectFdr	Filter: Select the p-values for an estimated false discovery rate. sklearn.feature_selection
SelectFpr	Filter: Select the pvalues below alpha based on a FPR test. sklearn.feature_selection
SelectFromModel	Meta-transformer for selecting features based on importance weights. sklearn.feature_selection
SelectFwe	Filter: Select the p-values corresponding to Family-wise error rate. sklearn.feature_selection
SelectKBest	Select features according to the k highest scores. sklearn.feature_selection
SelectPercentile	Select features according to a percentile of the highest scores. sklearn.feature_selection
SelectorMixin	Transformer mixin that performs feature selection given a support mask sklearn.feature_selection
SequentialFeatureSelector	Transformer that performs Sequential Feature Selection. sklearn.feature_selection
VarianceThreshold	Feature selector that removes all low-variance features. sklearn.feature_selection
chi2	Compute chi-squared stats between each non-negative feature and class. sklearn.feature_selection
f_classif	Compute the ANOVA F-value for the provided sample. sklearn.feature_selection
f_regression	Univariate linear regression tests returning F-statistic and p-values. sklearn.feature_selection
mutual_info_classif	Estimate mutual information for a discrete target variable. sklearn.feature_selection
mutual_info_regression	Estimate mutual information for a continuous target variable. sklearn.feature_selection
r_regression	Compute Pearson’s r for each features and the target. sklearn.feature_selection
GaussianProcessClassifier	Gaussian process classification (GPC) based on Laplace approximation. sklearn.gaussian_process
GaussianProcessRegressor	Gaussian process regression (GPR). sklearn.gaussian_process
CompoundKernel	Kernel which is composed of a set of other kernels. sklearn.gaussian_process.kernels
ConstantKernel	Constant kernel. sklearn.gaussian_process.kernels
DotProduct	Dot-Product kernel. sklearn.gaussian_process.kernels
ExpSineSquared	Exp-Sine-Squared kernel (aka periodic kernel). sklearn.gaussian_process.kernels
Exponentiation	The Exponentiation kernel takes one base kernel and a scalar parameter sklearn.gaussian_process.kernels
Hyperparameter	A kernel hyperparameter’s specification in form of a namedtuple. sklearn.gaussian_process.kernels
Kernel	Base class for all kernels. sklearn.gaussian_process.kernels
Matern	Matern kernel. sklearn.gaussian_process.kernels
PairwiseKernel	Wrapper for kernels in sklearn.metrics.pairwise. sklearn.gaussian_process.kernels
Product	The Product kernel takes two kernels \(k_1\) and \(k_2\) sklearn.gaussian_process.kernels
RBF	Radial basis function kernel (aka squared-exponential kernel). sklearn.gaussian_process.kernels
RationalQuadratic	Rational Quadratic kernel. sklearn.gaussian_process.kernels
Sum	The Sum kernel takes two kernels \(k_1\) and \(k_2\) sklearn.gaussian_process.kernels
WhiteKernel	White kernel. sklearn.gaussian_process.kernels
IterativeImputer	Multivariate imputer that estimates each feature from all the others. sklearn.impute
KNNImputer	Imputation for completing missing values using k-Nearest Neighbors. sklearn.impute
MissingIndicator	Binary indicators for missing values. sklearn.impute
SimpleImputer	Univariate imputer for completing missing values with simple strategies. sklearn.impute
partial_dependence	Partial dependence of features. sklearn.inspection
permutation_importance	Permutation importance for feature evaluation [Rd9e56ef97513-BRE]. sklearn.inspection
DecisionBoundaryDisplay	Decisions boundary visualization. sklearn.inspection
PartialDependenceDisplay	Partial Dependence Plot (PDP). sklearn.inspection
IsotonicRegression	Isotonic regression model. sklearn.isotonic
check_increasing	Determine whether y is monotonically correlated with x. sklearn.isotonic
isotonic_regression	Solve the isotonic regression model. sklearn.isotonic
AdditiveChi2Sampler	Approximate feature map for additive chi2 kernel. sklearn.kernel_approximation
Nystroem	Approximate a kernel map using a subset of the training data. sklearn.kernel_approximation
PolynomialCountSketch	Polynomial kernel approximation via Tensor Sketch. sklearn.kernel_approximation
RBFSampler	Approximate a RBF kernel feature map using random Fourier features. sklearn.kernel_approximation
SkewedChi2Sampler	Approximate feature map for “skewed chi-squared” kernel. sklearn.kernel_approximation
KernelRidge	Kernel ridge regression. sklearn.kernel_ridge
LogisticRegression	Logistic Regression (aka logit, MaxEnt) classifier. sklearn.linear_model
LogisticRegressionCV	Logistic Regression CV (aka logit, MaxEnt) classifier. sklearn.linear_model
PassiveAggressiveClassifier	Passive Aggressive Classifier. sklearn.linear_model
Perceptron	Linear perceptron classifier. sklearn.linear_model
RidgeClassifier	Classifier using Ridge regression. sklearn.linear_model
RidgeClassifierCV	Ridge classifier with built-in cross-validation. sklearn.linear_model
SGDClassifier	Linear classifiers (SVM, logistic regression, etc.) with SGD training. sklearn.linear_model
SGDOneClassSVM	Solves linear One-Class SVM using Stochastic Gradient Descent. sklearn.linear_model
LinearRegression	Ordinary least squares Linear Regression. sklearn.linear_model
Ridge	Linear least squares with l2 regularization. sklearn.linear_model
RidgeCV	Ridge regression with built-in cross-validation. sklearn.linear_model
SGDRegressor	Linear model fitted by minimizing a regularized empirical loss with SGD. sklearn.linear_model
ElasticNet	Linear regression with combined L1 and L2 priors as regularizer. sklearn.linear_model
ElasticNetCV	Elastic Net model with iterative fitting along a regularization path. sklearn.linear_model
Lars	Least Angle Regression model a.k.a. LAR. sklearn.linear_model
LarsCV	Cross-validated Least Angle Regression model. sklearn.linear_model
Lasso	Linear Model trained with L1 prior as regularizer (aka the Lasso). sklearn.linear_model
LassoCV	Lasso linear model with iterative fitting along a regularization path. sklearn.linear_model
LassoLars	Lasso model fit with Least Angle Regression a.k.a. Lars. sklearn.linear_model
LassoLarsCV	Cross-validated Lasso, using the LARS algorithm. sklearn.linear_model
LassoLarsIC	Lasso model fit with Lars using BIC or AIC for model selection. sklearn.linear_model
OrthogonalMatchingPursuit	Orthogonal Matching Pursuit model (OMP). sklearn.linear_model
OrthogonalMatchingPursuitCV	Cross-validated Orthogonal Matching Pursuit model (OMP). sklearn.linear_model
ARDRegression	Bayesian ARD regression. sklearn.linear_model
BayesianRidge	Bayesian ridge regression. sklearn.linear_model
MultiTaskElasticNet	Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer. sklearn.linear_model
MultiTaskElasticNetCV	Multi-task L1/L2 ElasticNet with built-in cross-validation. sklearn.linear_model
MultiTaskLasso	Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. sklearn.linear_model
MultiTaskLassoCV	Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer. sklearn.linear_model
HuberRegressor	L2-regularized linear regression model that is robust to outliers. sklearn.linear_model
QuantileRegressor	Linear regression model that predicts conditional quantiles. sklearn.linear_model
RANSACRegressor	RANSAC (RANdom SAmple Consensus) algorithm. sklearn.linear_model
TheilSenRegressor	Theil-Sen Estimator: robust multivariate regression model. sklearn.linear_model
GammaRegressor	Generalized Linear Model with a Gamma distribution. sklearn.linear_model
PoissonRegressor	Generalized Linear Model with a Poisson distribution. sklearn.linear_model
TweedieRegressor	Generalized Linear Model with a Tweedie distribution. sklearn.linear_model
PassiveAggressiveRegressor	Passive Aggressive Regressor. sklearn.linear_model
enet_path	Compute elastic net path with coordinate descent. sklearn.linear_model
lars_path	Compute Least Angle Regression or Lasso path using the LARS algorithm. sklearn.linear_model
lars_path_gram	The lars_path in the sufficient stats mode. sklearn.linear_model
lasso_path	Compute Lasso path with coordinate descent. sklearn.linear_model
orthogonal_mp	Orthogonal Matching Pursuit (OMP). sklearn.linear_model
orthogonal_mp_gram	Gram Orthogonal Matching Pursuit (OMP). sklearn.linear_model
ridge_regression	Solve the ridge equation by the method of normal equations. sklearn.linear_model
Isomap	Isomap Embedding. sklearn.manifold
LocallyLinearEmbedding	Locally Linear Embedding. sklearn.manifold
MDS	Multidimensional scaling. sklearn.manifold
SpectralEmbedding	Spectral embedding for non-linear dimensionality reduction. sklearn.manifold
TSNE	T-distributed Stochastic Neighbor Embedding. sklearn.manifold
locally_linear_embedding	Perform a Locally Linear Embedding analysis on the data. sklearn.manifold
smacof	Compute multidimensional scaling using the SMACOF algorithm. sklearn.manifold
spectral_embedding	Project the sample on the first eigenvectors of the graph Laplacian. sklearn.manifold
trustworthiness	Indicate to what extent the local structure is retained. sklearn.manifold
check_scoring	Determine scorer from user options. sklearn.metrics
get_scorer	Get a scorer from string. sklearn.metrics
get_scorer_names	Get the names of all available scorers. sklearn.metrics
make_scorer	Make a scorer from a performance metric or loss function. sklearn.metrics
accuracy_score	Accuracy classification score. sklearn.metrics
auc	Compute Area Under the Curve (AUC) using the trapezoidal rule. sklearn.metrics
average_precision_score	Compute average precision (AP) from prediction scores. sklearn.metrics
balanced_accuracy_score	Compute the balanced accuracy. sklearn.metrics
brier_score_loss	Compute the Brier score loss. sklearn.metrics
class_likelihood_ratios	Compute binary classification positive and negative likelihood ratios. sklearn.metrics
classification_report	Build a text report showing the main classification metrics. sklearn.metrics
cohen_kappa_score	Compute Cohen’s kappa: a statistic that measures inter-annotator agreement. sklearn.metrics
confusion_matrix	Compute confusion matrix to evaluate the accuracy of a classification. sklearn.metrics
d2_log_loss_score	\(D^2\) score function, fraction of log loss explained. sklearn.metrics
dcg_score	Compute Discounted Cumulative Gain. sklearn.metrics
det_curve	Compute error rates for different probability thresholds. sklearn.metrics
f1_score	Compute the F1 score, also known as balanced F-score or F-measure. sklearn.metrics
fbeta_score	Compute the F-beta score. sklearn.metrics
hamming_loss	Compute the average Hamming loss. sklearn.metrics
hinge_loss	Average hinge loss (non-regularized). sklearn.metrics
jaccard_score	Jaccard similarity coefficient score. sklearn.metrics
log_loss	Log loss, aka logistic loss or cross-entropy loss. sklearn.metrics
matthews_corrcoef	Compute the Matthews correlation coefficient (MCC). sklearn.metrics
multilabel_confusion_matrix	Compute a confusion matrix for each class or sample. sklearn.metrics
ndcg_score	Compute Normalized Discounted Cumulative Gain. sklearn.metrics
precision_recall_curve	Compute precision-recall pairs for different probability thresholds. sklearn.metrics
precision_recall_fscore_support	Compute precision, recall, F-measure and support for each class. sklearn.metrics
precision_score	Compute the precision. sklearn.metrics
recall_score	Compute the recall. sklearn.metrics
roc_auc_score	Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores. sklearn.metrics
roc_curve	Compute Receiver operating characteristic (ROC). sklearn.metrics
top_k_accuracy_score	Top-k Accuracy classification score. sklearn.metrics
zero_one_loss	Zero-one classification loss. sklearn.metrics
d2_absolute_error_score	\(D^2\) regression score function, fraction of absolute error explained. sklearn.metrics
d2_pinball_score	\(D^2\) regression score function, fraction of pinball loss explained. sklearn.metrics
d2_tweedie_score	\(D^2\) regression score function, fraction of Tweedie deviance explained. sklearn.metrics
explained_variance_score	Explained variance regression score function. sklearn.metrics
max_error	The max_error metric calculates the maximum residual error. sklearn.metrics
mean_absolute_error	Mean absolute error regression loss. sklearn.metrics
mean_absolute_percentage_error	Mean absolute percentage error (MAPE) regression loss. sklearn.metrics
mean_gamma_deviance	Mean Gamma deviance regression loss. sklearn.metrics
mean_pinball_loss	Pinball loss for quantile regression. sklearn.metrics
mean_poisson_deviance	Mean Poisson deviance regression loss. sklearn.metrics
mean_squared_error	Mean squared error regression loss. sklearn.metrics
mean_squared_log_error	Mean squared logarithmic error regression loss. sklearn.metrics
mean_tweedie_deviance	Mean Tweedie deviance regression loss. sklearn.metrics
median_absolute_error	Median absolute error regression loss. sklearn.metrics
r2_score	\(R^2\) (coefficient of determination) regression score function. sklearn.metrics
root_mean_squared_error	Root mean squared error regression loss. sklearn.metrics
root_mean_squared_log_error	Root mean squared logarithmic error regression loss. sklearn.metrics
coverage_error	Coverage error measure. sklearn.metrics
label_ranking_average_precision_score	Compute ranking-based average precision. sklearn.metrics
label_ranking_loss	Compute Ranking loss measure. sklearn.metrics
adjusted_mutual_info_score	Adjusted Mutual Information between two clusterings. sklearn.metrics
adjusted_rand_score	Rand index adjusted for chance. sklearn.metrics
calinski_harabasz_score	Compute the Calinski and Harabasz score. sklearn.metrics
contingency_matrix	Build a contingency matrix describing the relationship between labels. sklearn.metrics.cluster
pair_confusion_matrix	Pair confusion matrix arising from two clusterings. sklearn.metrics.cluster
completeness_score	Compute completeness metric of a cluster labeling given a ground truth. sklearn.metrics
davies_bouldin_score	Compute the Davies-Bouldin score. sklearn.metrics
fowlkes_mallows_score	Measure the similarity of two clusterings of a set of points. sklearn.metrics
homogeneity_completeness_v_measure	Compute the homogeneity and completeness and V-Measure scores at once. sklearn.metrics
homogeneity_score	Homogeneity metric of a cluster labeling given a ground truth. sklearn.metrics
mutual_info_score	Mutual Information between two clusterings. sklearn.metrics
normalized_mutual_info_score	Normalized Mutual Information between two clusterings. sklearn.metrics
rand_score	Rand index. sklearn.metrics
silhouette_samples	Compute the Silhouette Coefficient for each sample. sklearn.metrics
silhouette_score	Compute the mean Silhouette Coefficient of all samples. sklearn.metrics
v_measure_score	V-measure cluster labeling given a ground truth. sklearn.metrics
consensus_score	The similarity of two sets of biclusters. sklearn.metrics
DistanceMetric	Uniform interface for fast distance metric functions. sklearn.metrics
additive_chi2_kernel	Compute the additive chi-squared kernel between observations in X and Y. sklearn.metrics.pairwise
chi2_kernel	Compute the exponential chi-squared kernel between X and Y. sklearn.metrics.pairwise
cosine_distances	Compute cosine distance between samples in X and Y. sklearn.metrics.pairwise
cosine_similarity	Compute cosine similarity between samples in X and Y. sklearn.metrics.pairwise
distance_metrics	Valid metrics for pairwise_distances. sklearn.metrics.pairwise
euclidean_distances	Compute the distance matrix between each pair from a vector array X and Y. sklearn.metrics.pairwise
haversine_distances	Compute the Haversine distance between samples in X and Y. sklearn.metrics.pairwise
kernel_metrics	Valid metrics for pairwise_kernels. sklearn.metrics.pairwise
laplacian_kernel	Compute the laplacian kernel between X and Y. sklearn.metrics.pairwise
linear_kernel	Compute the linear kernel between X and Y. sklearn.metrics.pairwise
manhattan_distances	Compute the L1 distances between the vectors in X and Y. sklearn.metrics.pairwise
nan_euclidean_distances	Calculate the euclidean distances in the presence of missing values. sklearn.metrics.pairwise
paired_cosine_distances	Compute the paired cosine distances between X and Y. sklearn.metrics.pairwise
paired_distances	Compute the paired distances between X and Y. sklearn.metrics.pairwise
paired_euclidean_distances	Compute the paired euclidean distances between X and Y. sklearn.metrics.pairwise
paired_manhattan_distances	Compute the paired L1 distances between X and Y. sklearn.metrics.pairwise
pairwise_kernels	Compute the kernel between arrays X and optional array Y. sklearn.metrics.pairwise
polynomial_kernel	Compute the polynomial kernel between X and Y. sklearn.metrics.pairwise
rbf_kernel	Compute the rbf (gaussian) kernel between X and Y. sklearn.metrics.pairwise
sigmoid_kernel	Compute the sigmoid kernel between X and Y. sklearn.metrics.pairwise
pairwise_distances	Compute the distance matrix from a vector array X and optional Y. sklearn.metrics
pairwise_distances_argmin	Compute minimum distances between one point and a set of points. sklearn.metrics
pairwise_distances_argmin_min	Compute minimum distances between one point and a set of points. sklearn.metrics
pairwise_distances_chunked	Generate a distance matrix chunk by chunk with optional reduction. sklearn.metrics
ConfusionMatrixDisplay	Confusion Matrix visualization. sklearn.metrics
DetCurveDisplay	DET curve visualization. sklearn.metrics
PrecisionRecallDisplay	Precision Recall visualization. sklearn.metrics
PredictionErrorDisplay	Visualization of the prediction error of a regression model. sklearn.metrics
RocCurveDisplay	ROC Curve visualization. sklearn.metrics
BayesianGaussianMixture	Variational Bayesian estimation of a Gaussian mixture. sklearn.mixture
GaussianMixture	Gaussian Mixture. sklearn.mixture
GroupKFold	K-fold iterator variant with non-overlapping groups. sklearn.model_selection
GroupShuffleSplit	Shuffle-Group(s)-Out cross-validation iterator. sklearn.model_selection
KFold	K-Fold cross-validator. sklearn.model_selection
LeaveOneGroupOut	Leave One Group Out cross-validator. sklearn.model_selection
LeaveOneOut	Leave-One-Out cross-validator. sklearn.model_selection
LeavePGroupsOut	Leave P Group(s) Out cross-validator. sklearn.model_selection
LeavePOut	Leave-P-Out cross-validator. sklearn.model_selection
PredefinedSplit	Predefined split cross-validator. sklearn.model_selection
RepeatedKFold	Repeated K-Fold cross validator. sklearn.model_selection
RepeatedStratifiedKFold	Repeated Stratified K-Fold cross validator. sklearn.model_selection
ShuffleSplit	Random permutation cross-validator. sklearn.model_selection
StratifiedGroupKFold	Stratified K-Fold iterator variant with non-overlapping groups. sklearn.model_selection
StratifiedKFold	Stratified K-Fold cross-validator. sklearn.model_selection
StratifiedShuffleSplit	Stratified ShuffleSplit cross-validator. sklearn.model_selection
TimeSeriesSplit	Time Series cross-validator. sklearn.model_selection
check_cv	Input checker utility for building a cross-validator. sklearn.model_selection
train_test_split	Split arrays or matrices into random train and test subsets. sklearn.model_selection
GridSearchCV	Exhaustive search over specified parameter values for an estimator. sklearn.model_selection
HalvingGridSearchCV	Search over specified parameter values with successive halving. sklearn.model_selection
HalvingRandomSearchCV	Randomized search on hyper parameters. sklearn.model_selection
ParameterGrid	Grid of parameters with a discrete number of values for each. sklearn.model_selection
ParameterSampler	Generator on parameters sampled from given distributions. sklearn.model_selection
RandomizedSearchCV	Randomized search on hyper parameters. sklearn.model_selection
FixedThresholdClassifier	Binary classifier that manually sets the decision threshold. sklearn.model_selection
TunedThresholdClassifierCV	Classifier that post-tunes the decision threshold using cross-validation. sklearn.model_selection
cross_val_predict	Generate cross-validated estimates for each input data point. sklearn.model_selection
cross_val_score	Evaluate a score by cross-validation. sklearn.model_selection
cross_validate	Evaluate metric(s) by cross-validation and also record fit/score times. sklearn.model_selection
learning_curve	Learning curve. sklearn.model_selection
permutation_test_score	Evaluate the significance of a cross-validated score with permutations. sklearn.model_selection
validation_curve	Validation curve. sklearn.model_selection
LearningCurveDisplay	Learning Curve visualization. sklearn.model_selection
ValidationCurveDisplay	Validation Curve visualization. sklearn.model_selection
OneVsOneClassifier	One-vs-one multiclass strategy. sklearn.multiclass
OneVsRestClassifier	One-vs-the-rest (OvR) multiclass strategy. sklearn.multiclass
OutputCodeClassifier	(Error-Correcting) Output-Code multiclass strategy. sklearn.multiclass
ClassifierChain	A multi-label model that arranges binary classifiers into a chain. sklearn.multioutput
MultiOutputClassifier	Multi target classification. sklearn.multioutput
MultiOutputRegressor	Multi target regression. sklearn.multioutput
RegressorChain	A multi-label model that arranges regressions into a chain. sklearn.multioutput
BernoulliNB	Naive Bayes classifier for multivariate Bernoulli models. sklearn.naive_bayes
CategoricalNB	Naive Bayes classifier for categorical features. sklearn.naive_bayes
ComplementNB	The Complement Naive Bayes classifier described in Rennie et al. (2003). sklearn.naive_bayes
GaussianNB	Gaussian Naive Bayes (GaussianNB). sklearn.naive_bayes
MultinomialNB	Naive Bayes classifier for multinomial models. sklearn.naive_bayes
BallTree	BallTree for fast generalized N-point problems sklearn.neighbors
KDTree	KDTree for fast generalized N-point problems sklearn.neighbors
KNeighborsClassifier	Classifier implementing the k-nearest neighbors vote. sklearn.neighbors
KNeighborsRegressor	Regression based on k-nearest neighbors. sklearn.neighbors
KNeighborsTransformer	Transform X into a (weighted) graph of k nearest neighbors. sklearn.neighbors
KernelDensity	Kernel Density Estimation. sklearn.neighbors
LocalOutlierFactor	Unsupervised Outlier Detection using the Local Outlier Factor (LOF). sklearn.neighbors
NearestCentroid	Nearest centroid classifier. sklearn.neighbors
NearestNeighbors	Unsupervised learner for implementing neighbor searches. sklearn.neighbors
NeighborhoodComponentsAnalysis	Neighborhood Components Analysis. sklearn.neighbors
RadiusNeighborsClassifier	Classifier implementing a vote among neighbors within a given radius. sklearn.neighbors
RadiusNeighborsRegressor	Regression based on neighbors within a fixed radius. sklearn.neighbors
RadiusNeighborsTransformer	Transform X into a (weighted) graph of neighbors nearer than a radius. sklearn.neighbors
kneighbors_graph	Compute the (weighted) graph of k-Neighbors for points in X. sklearn.neighbors
radius_neighbors_graph	Compute the (weighted) graph of Neighbors for points in X. sklearn.neighbors
sort_graph_by_row_values	Sort a sparse graph such that each row is stored with increasing values. sklearn.neighbors
BernoulliRBM	Bernoulli Restricted Boltzmann Machine (RBM). sklearn.neural_network
MLPClassifier	Multi-layer Perceptron classifier. sklearn.neural_network
MLPRegressor	Multi-layer Perceptron regressor. sklearn.neural_network
FeatureUnion	Concatenates results of multiple transformer objects. sklearn.pipeline
Pipeline	A sequence of data transformers with an optional final predictor. sklearn.pipeline
make_pipeline	Construct a Pipeline from the given estimators. sklearn.pipeline
make_union	Construct a FeatureUnion from the given transformers. sklearn.pipeline
Binarizer	Binarize data (set feature values to 0 or 1) according to a threshold. sklearn.preprocessing
FunctionTransformer	Constructs a transformer from an arbitrary callable. sklearn.preprocessing
KBinsDiscretizer	Bin continuous data into intervals. sklearn.preprocessing
KernelCenterer	Center an arbitrary kernel matrix \(K\). sklearn.preprocessing
LabelBinarizer	Binarize labels in a one-vs-all fashion. sklearn.preprocessing
LabelEncoder	Encode target labels with value between 0 and n_classes-1. sklearn.preprocessing
MaxAbsScaler	Scale each feature by its maximum absolute value. sklearn.preprocessing
MinMaxScaler	Transform features by scaling each feature to a given range. sklearn.preprocessing
MultiLabelBinarizer	Transform between iterable of iterables and a multilabel format. sklearn.preprocessing
Normalizer	Normalize samples individually to unit norm. sklearn.preprocessing
OneHotEncoder	Encode categorical features as a one-hot numeric array. sklearn.preprocessing
OrdinalEncoder	Encode categorical features as an integer array. sklearn.preprocessing
PolynomialFeatures	Generate polynomial and interaction features. sklearn.preprocessing
PowerTransformer	Apply a power transform featurewise to make data more Gaussian-like. sklearn.preprocessing
QuantileTransformer	Transform features using quantiles information. sklearn.preprocessing
RobustScaler	Scale features using statistics that are robust to outliers. sklearn.preprocessing
SplineTransformer	Generate univariate B-spline bases for features. sklearn.preprocessing
StandardScaler	Standardize features by removing the mean and scaling to unit variance. sklearn.preprocessing
TargetEncoder	Target Encoder for regression and classification targets. sklearn.preprocessing
add_dummy_feature	Augment dataset with an additional dummy feature. sklearn.preprocessing
binarize	Boolean thresholding of array-like or scipy.sparse matrix. sklearn.preprocessing
label_binarize	Binarize labels in a one-vs-all fashion. sklearn.preprocessing
maxabs_scale	Scale each feature to the [-1, 1] range without breaking the sparsity. sklearn.preprocessing
minmax_scale	Transform features by scaling each feature to a given range. sklearn.preprocessing
normalize	Scale input vectors individually to unit norm (vector length). sklearn.preprocessing
power_transform	Parametric, monotonic transformation to make data more Gaussian-like. sklearn.preprocessing
quantile_transform	Transform features using quantiles information. sklearn.preprocessing
robust_scale	Standardize a dataset along any axis. sklearn.preprocessing
scale	Standardize a dataset along any axis. sklearn.preprocessing
GaussianRandomProjection	Reduce dimensionality through Gaussian random projection. sklearn.random_projection
SparseRandomProjection	Reduce dimensionality through sparse random projection. sklearn.random_projection
johnson_lindenstrauss_min_dim	Find a ‘safe’ number of components to randomly project to. sklearn.random_projection
LabelPropagation	Label Propagation classifier. sklearn.semi_supervised
LabelSpreading	LabelSpreading model for semi-supervised learning. sklearn.semi_supervised
SelfTrainingClassifier	Self-training classifier. sklearn.semi_supervised
LinearSVC	Linear Support Vector Classification. sklearn.svm
LinearSVR	Linear Support Vector Regression. sklearn.svm
NuSVC	Nu-Support Vector Classification. sklearn.svm
NuSVR	Nu Support Vector Regression. sklearn.svm
OneClassSVM	Unsupervised Outlier Detection. sklearn.svm
SVC	C-Support Vector Classification. sklearn.svm
SVR	Epsilon-Support Vector Regression. sklearn.svm
l1_min_c	Return the lowest bound for C. sklearn.svm
DecisionTreeClassifier	A decision tree classifier. sklearn.tree
DecisionTreeRegressor	A decision tree regressor. sklearn.tree
ExtraTreeClassifier	An extremely randomized tree classifier. sklearn.tree
ExtraTreeRegressor	An extremely randomized tree regressor. sklearn.tree
export_graphviz	Export a decision tree in DOT format. sklearn.tree
export_text	Build a text report showing the rules of a decision tree. sklearn.tree
plot_tree	Plot a decision tree. sklearn.tree
Bunch	Container object exposing keys as attributes. sklearn.utils
_safe_indexing	Return rows, items or columns of X using indices. sklearn.utils
as_float_array	Convert an array-like to an array of floats. sklearn.utils
assert_all_finite	Throw a ValueError if X contains NaN or infinity. sklearn.utils
deprecated	Decorator to mark a function or class as deprecated. sklearn.utils
estimator_html_repr	Build a HTML representation of an estimator. sklearn.utils
gen_batches	Generator to create slices containing batch_size elements from 0 to n. sklearn.utils
gen_even_slices	Generator to create n_packs evenly spaced slices going up to n. sklearn.utils
indexable	Make arrays indexable for cross-validation. sklearn.utils
murmurhash3_32	Compute the 32bit murmurhash3 of key at seed. sklearn.utils
resample	Resample arrays or sparse matrices in a consistent way. sklearn.utils
safe_mask	Return a mask which is safe to use on X. sklearn.utils
safe_sqr	Element wise squaring of array-likes and sparse matrices. sklearn.utils
shuffle	Shuffle arrays or sparse matrices in a consistent way. sklearn.utils
Tags	Tags for the estimator. sklearn.utils
InputTags	Tags for the input data. sklearn.utils
TargetTags	Tags for the target data. sklearn.utils
ClassifierTags	Tags for the classifier. sklearn.utils
RegressorTags	Tags for the regressor. sklearn.utils
TransformerTags	Tags for the transformer. sklearn.utils
default_tags	Get the default tags for an estimator. sklearn.utils
get_tags	Get estimator tags. sklearn.utils
check_X_y	Input validation for standard estimators. sklearn.utils
check_array	Input validation on an array, list, sparse matrix or similar. sklearn.utils
check_consistent_length	check that all arrays have consistent first dimensions. sklearn.utils
check_random_state	Turn seed into a np.random.RandomState instance. sklearn.utils
check_scalar	Validate scalar parameters type and value. sklearn.utils
check_is_fitted	Perform is_fitted validation for estimator. sklearn.utils.validation
check_memory	check that memory is joblib.Memory-like. sklearn.utils.validation
check_symmetric	Make sure that array is 2D, square and symmetric. sklearn.utils.validation
column_or_1d	Ravel column or 1d numpy array, else raises an error. sklearn.utils.validation
has_fit_parameter	check whether the estimator’s fit method supports the given parameter. sklearn.utils.validation
validate_data	Validate input data and set or check feature names and counts of the input. sklearn.utils.validation
available_if	An attribute that is available only if check returns a truthy value. sklearn.utils.metaestimators
compute_class_weight	Estimate class weights for unbalanced datasets. sklearn.utils.class_weight
compute_sample_weight	Estimate sample weights by class for unbalanced datasets. sklearn.utils.class_weight
is_multilabel	check if y is in a multilabel format. sklearn.utils.multiclass
type_of_target	Determine the type of data indicated by the target. sklearn.utils.multiclass
unique_labels	Extract an ordered array of unique labels. sklearn.utils.multiclass
density	Compute density of a sparse vector. sklearn.utils.extmath
fast_logdet	Compute logarithm of determinant of a square matrix. sklearn.utils.extmath
randomized_range_finder	Compute an orthonormal matrix whose range approximates the range of A. sklearn.utils.extmath
randomized_svd	Compute a truncated randomized SVD. sklearn.utils.extmath
safe_sparse_dot	Dot product that handle the sparse matrix case correctly. sklearn.utils.extmath
weighted_mode	Return an array of the weighted modal (most common) value in the passed array. sklearn.utils.extmath
incr_mean_variance_axis	Compute incremental mean and variance along an axis on a CSR or CSC matrix. sklearn.utils.sparsefuncs
inplace_column_scale	Inplace column scaling of a CSC/CSR matrix. sklearn.utils.sparsefuncs
inplace_csr_column_scale	Inplace column scaling of a CSR matrix. sklearn.utils.sparsefuncs
inplace_row_scale	Inplace row scaling of a CSR or CSC matrix. sklearn.utils.sparsefuncs
inplace_swap_column	Swap two columns of a CSC/CSR matrix in-place. sklearn.utils.sparsefuncs
inplace_swap_row	Swap two rows of a CSC/CSR matrix in-place. sklearn.utils.sparsefuncs
mean_variance_axis	Compute mean and variance along an axis on a CSR or CSC matrix. sklearn.utils.sparsefuncs
inplace_csr_row_normalize_l1	Normalize inplace the rows of a CSR matrix or array by their L1 norm. sklearn.utils.sparsefuncs_fast
inplace_csr_row_normalize_l2	Normalize inplace the rows of a CSR matrix or array by their L2 norm. sklearn.utils.sparsefuncs_fast
single_source_shortest_path_length	Return the length of the shortest path from source to all reachable nodes. sklearn.utils.graph
sample_without_replacement	Sample integers without replacement. sklearn.utils.random
min_pos	Find the minimum value of an array over positive values. sklearn.utils.arrayfuncs
MetadataRequest	Contains the metadata request info of a consumer. sklearn.utils.metadata_routing
MetadataRouter	Stores and handles metadata routing for a router object. sklearn.utils.metadata_routing
MethodMapping	Stores the mapping between caller and callee methods for a router. sklearn.utils.metadata_routing
get_routing_for_object	Get a Metadata{Router, Request} instance from the given object. sklearn.utils.metadata_routing
process_routing	Validate and route input parameters. sklearn.utils.metadata_routing
all_displays	Get a list of all displays from sklearn. sklearn.utils.discovery
all_estimators	Get a list of all estimators from sklearn. sklearn.utils.discovery
all_functions	Get a list of all functions from sklearn. sklearn.utils.discovery
check_estimator	check if estimator adheres to scikit-learn conventions. sklearn.utils.estimator_checks
parametrize_with_checks	Pytest specific decorator for parametrizing estimator checks. sklearn.utils.estimator_checks
Parallel	Tweak of joblib.Parallel that propagates the scikit-learn configuration. sklearn.utils.parallel
delayed	Decorator used to capture the arguments of a function. sklearn.utils.parallel
parallel_backend	Change the default backend used by Parallel inside a with block. sklearn.utils Deprecated in version 1.7
register_parallel_backend	Register a new Parallel backend factory. sklearn.utils Deprecated in version 1.7