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 use. For reference on concepts repeated across the API, see Glossary of Common Terms and API Elements.

Object	Description
config_context	Context manager to temporarily change the global scikit-learn configuration. sklearn
get_config	Retrieve the current scikit-learn configuration. 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
is_outlier_detector	Return True if the given estimator is (probably) an outlier detector. sklearn.base
CalibratedClassifierCV	Calibrate probabilities using isotonic, sigmoid, or temperature scaling. 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) [R2af9f5762274-1]. sklearn.cluster
SpectralClustering	Apply clustering to a projection of the normalized Laplacian. sklearn.cluster
SpectralCoclustering	Spectral Co-Clustering algorithm (Dhillon, 2001) [R0dd0f3306ba7-1]. 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 an 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
EstimatorCheckFailedWarning	Warning raised when an estimator check from the common tests fails. 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