class sklearn.isotonic.IsotonicRegression(y_min=None, y_max=None, increasing=True, out_of_bounds='nan')

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False

sklearn.metrics.jaccard_similarity_score(y_true, y_pred, normalize=True, sample_weight=None)

class sklearn.linear_model.LarsCV(fit_intercept=True, verbose=False, max_iter=500, normalize=True, precompute='auto', cv=None, max_n_alphas=1000

class sklearn.preprocessing.RobustScaler(with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

class sklearn.decomposition.MiniBatchDictionaryLearning(n_components=None, alpha=1, n_iter=1000, fit_algorithm='lars'

class sklearn.cross_decomposition.PLSRegression(n_components=2, scale=True, max_iter=500, tol=1e-06, copy=True)

class sklearn.feature_extraction.text.HashingVectorizer(input=u'content', encoding=u'utf-8', decode_error=u'strict'

class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05

class sklearn.tree.DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1