sklearn.datasets.make_s_curve(n_samples=100, noise=0.0, random_state=None)

sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)

sklearn.datasets.dump_svmlight_file(X, y, f, zero_based=True, comment=None, query_id=None, multilabel=False)

sklearn.datasets.fetch_20newsgroups_vectorized(subset='train', remove=(), data_home=None)

sklearn.datasets.fetch_rcv1(data_home=None, subset='all', download_if_missing=True, random_state=None, shuffle=False)

sklearn.datasets.load_files(container_path, description=None, categories=None, load_content=True, shuffle=True, encoding=None, decode_error='strict'

sklearn.datasets.load_mlcomp(name_or_id, set_='raw', mlcomp_root=None, **kwargs)

sklearn.datasets.fetch_species_distributions(data_home=None, download_if_missing=True)

sklearn.datasets.make_checkerboard(shape, n_clusters, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None)

sklearn.datasets.fetch_kddcup99(subset=None, shuffle=False, random_state=None, percent10=True, download_if_missing=True)