class sklearn.linear_model.HuberRegressor(epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05)

class sklearn.linear_model.PassiveAggressiveRegressor(C=1.0, fit_intercept=True, n_iter=5, shuffle=True, verbose=0

class sklearn.linear_model.BayesianRidge(n_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, compute_score=False

class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, l1_ratio=0.5, fit_intercept=True, normalize=False, copy_X=True,

class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None

class sklearn.linear_model.RANSACRegressor(base_estimator=None, min_samples=None, residual_threshold=None, is_data_valid=None

class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize=False, precompute='auto', max_iter=1000

class sklearn.linear_model.LogisticRegressionCV(Cs=10, fit_intercept=True, cv=None, dual=False, penalty='l2', scoring=None

class sklearn.linear_model.LassoLarsIC(criterion='aic', fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500

sklearn.linear_model.lars_path(X, y, Xy=None, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.2204460492503131e-16