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

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

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

class sklearn.linear_model.Lars(fit_intercept=True, verbose=False, normalize=True, precompute='auto', n_nonzero_coefs=500, eps=2.2204460492503131e-16

sklearn.linear_model.lasso_stability_path(X, y, scaling=0.5, random_state=None, n_resampling=200, n_grid=100, sample_fraction=0

class sklearn.linear_model.LassoLars(alpha=1.0, fit_intercept=True, verbose=False, normalize=True, precompute='auto', max_iter=500,

class sklearn.linear_model.Perceptron(penalty=None, alpha=0.0001, fit_intercept=True, n_iter=5, shuffle=True, verbose=0, eta0=1.0,

class sklearn.linear_model.LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1

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.linear_model.ARDRegression(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