sklearn.datasets

Utilities to load popular datasets and artificial data generators.

User guide. See the Dataset loading utilities section for further details.

Loaders

clear_data_home	Delete all the content of the data home cache.
dump_svmlight_file	Dump the dataset in svmlight / libsvm file format.
fetch_20newsgroups	Load the filenames and data from the 20 newsgroups dataset (classification).
fetch_20newsgroups_vectorized	Load and vectorize the 20 newsgroups dataset (classification).
fetch_california_housing	Load the California housing dataset (regression).
fetch_covtype	Load the covertype dataset (classification).
fetch_file	Fetch a file from the web if not already present in the local folder.
fetch_kddcup99	Load the kddcup99 dataset (classification).
fetch_lfw_pairs	Load the Labeled Faces in the Wild (LFW) pairs dataset (classification).
fetch_lfw_people	Load the Labeled Faces in the Wild (LFW) people dataset (classification).
fetch_olivetti_faces	Load the Olivetti faces data-set from AT&T (classification).
fetch_openml	Fetch dataset from openml by name or dataset id.
fetch_rcv1	Load the RCV1 multilabel dataset (classification).
fetch_species_distributions	Loader for species distribution dataset from Phillips et.
get_data_home	Return the path of the scikit-learn data directory.
load_breast_cancer	Load and return the breast cancer wisconsin dataset (classification).
load_diabetes	Load and return the diabetes dataset (regression).
load_digits	Load and return the digits dataset (classification).
load_files	Load text files with categories as subfolder names.
load_iris	Load and return the iris dataset (classification).
load_linnerud	Load and return the physical exercise Linnerud dataset.
load_sample_image	Load the numpy array of a single sample image.
load_sample_images	Load sample images for image manipulation.
load_svmlight_file	Load datasets in the svmlight / libsvm format into sparse CSR matrix.
load_svmlight_files	Load dataset from multiple files in SVMlight format.
load_wine	Load and return the wine dataset (classification).

Sample generators

make_biclusters	Generate a constant block diagonal structure array for biclustering.
make_blobs	Generate isotropic Gaussian blobs for clustering.
make_checkerboard	Generate an array with block checkerboard structure for biclustering.
make_circles	Make a large circle containing a smaller circle in 2d.
make_classification	Generate a random n-class classification problem.
make_friedman1	Generate the "Friedman #1" regression problem.
make_friedman2	Generate the "Friedman #2" regression problem.
make_friedman3	Generate the "Friedman #3" regression problem.
make_gaussian_quantiles	Generate isotropic Gaussian and label samples by quantile.
make_hastie_10_2	Generate data for binary classification used in Hastie et al. 2009, Example 10.2.
make_low_rank_matrix	Generate a mostly low rank matrix with bell-shaped singular values.
make_moons	Make two interleaving half circles.
make_multilabel_classification	Generate a random multilabel classification problem.
make_regression	Generate a random regression problem.
make_s_curve	Generate an S curve dataset.
make_sparse_coded_signal	Generate a signal as a sparse combination of dictionary elements.
make_sparse_spd_matrix	Generate a sparse symmetric definite positive matrix.
make_sparse_uncorrelated	Generate a random regression problem with sparse uncorrelated design.
make_spd_matrix	Generate a random symmetric, positive-definite matrix.
make_swiss_roll	Generate a swiss roll dataset.