Wrapper for sklearn datasets/data generators.


skl_biclusters(shape, n_clusters[, noise, ...]) Generate an array with constant block diagonal structure for biclustering.
skl_blobs([n_samples, n_features, centers, ...]) Generate isotropic Gaussian blobs for clustering.
skl_boston() Load and return the boston house-prices dataset (regression).
skl_checkerboard(shape, n_clusters[, noise, ...]) Generate an array with block checkerboard structure for biclustering.
skl_circles([n_samples, shuffle, noise, ...]) Make a large circle containing a smaller circle in 2d.
skl_classification([n_samples, n_features, ...]) Generate a random n-class classification problem.
skl_diabetes() Load and return the diabetes dataset (regression).
skl_digits([n_class]) Load and return the digits dataset (classification).
skl_friedman1([n_samples, n_features, ...]) Generate the “Friedman #1” regression problem
skl_friedman2([n_samples, noise, random_state]) Generate the “Friedman #2” regression problem
skl_friedman3([n_samples, noise, random_state]) Generate the “Friedman #3” regression problem
skl_gaussian_quantiles([mean, cov, ...]) Generate isotropic Gaussian and label samples by quantile
skl_hastie_10_2([n_samples, random_state]) Generates data for binary classification used in Hastie et al.
skl_iris() Load and return the iris dataset (classification).
skl_lfw_pairs([download_if_missing]) Alias for fetch_lfw_pairs(download_if_missing=False)
skl_lfw_people([download_if_missing]) Alias for fetch_lfw_people(download_if_missing=False)
skl_linnerud() Load and return the linnerud dataset (multivariate regression).
skl_low_rank_matrix([n_samples, n_features, ...]) Generate a mostly low rank matrix with bell-shaped singular values
skl_mlcomp(name_or_id[, set_, mlcomp_root]) Load a datasets as downloaded from
skl_moons([n_samples, shuffle, noise, ...]) Make two interleaving half circles
skl_multilabel_classification([n_samples, ...]) Generate a random multilabel classification problem.
skl_regression([n_samples, n_features, ...]) Generate a random regression problem.
skl_s_curve([n_samples, noise, random_state]) Generate an S curve dataset.
skl_sparse_coded_signal(n_samples, ...[, ...]) Generate a signal as a sparse combination of dictionary elements.
skl_sparse_spd_matrix([dim, alpha, ...]) Generate a sparse symmetric definite positive matrix.
skl_sparse_uncorrelated([n_samples, ...]) Generate a random regression problem with sparse uncorrelated design
skl_spd_matrix(n_dim[, random_state]) Generate a random symmetric, positive-definite matrix.
skl_swiss_roll([n_samples, noise, random_state]) Generate a swiss roll dataset.