class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None

class sklearn.decomposition.NMF(n_components=None, init=None, solver='cd', tol=0.0001, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0

sklearn.svm.libsvm.predict_proba() Predict probabilities svm_model stores all parameters needed to

class sklearn.preprocessing.KernelCenterer

sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True

class sklearn.cluster.DBSCAN(eps=0.5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, n_jobs=1)

class sklearn.neighbors.RadiusNeighborsClassifier(radius=1.0, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski'

class sklearn.decomposition.DictionaryLearning(n_components=None, alpha=1, max_iter=1000, tol=1e-08, fit_algorithm='lars'

Warning DEPRECATED

sklearn.datasets.load_sample_images()