sklearn.datasets.fetch_mldata(dataname, target_name='label', data_name='data', transpose_data=True, data_home=None)

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

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

sklearn.datasets.load_iris(return_X_y=False)

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

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

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