sklearn.datasets.load_boston(return_X_y=False)

sklearn.datasets.load_iris(return_X_y=False)

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_sparse_uncorrelated(n_samples=100, n_features=10, random_state=None)

sklearn.datasets.make_low_rank_matrix(n_samples=100, n_features=100, effective_rank=10, tail_strength=0.5, random_state=None)

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

sklearn.datasets.make_regression(n_samples=100, n_features=100, n_informative=10, n_targets=1, bias=0.0, effective_rank=None, tail_strength=0

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

sklearn.datasets.load_sample_images()