sklearn.datasets.make_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None)

sklearn.datasets.make_sparse_spd_matrix(dim=1, alpha=0.95, norm_diag=False, smallest_coef=0.1, largest_coef=0.9, random_state=None)

sklearn.datasets.make_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)

sklearn.datasets.load_sample_images()

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

sklearn.datasets.fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, remove=()

sklearn.datasets.load_svmlight_files(files, n_features=None, dtype=, multilabel=False, zero_based='auto', query_id=False)

sklearn.datasets.load_boston(return_X_y=False)

sklearn.datasets.load_iris(return_X_y=False)