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lars_path sklearn.linear_model.lars_path(X, y, Xy=None, *, Gram=None, max_iter=500, alpha_min=0, method='lar', copy_X=True, eps=2.220446049250313e-16, copy_Gram=True, verbose=0, return_path=True, return_n_iter=False, positive=False) lars_path 파라미터 XNone or array-like of shape (n_samples, n_features) yNone or array-like of shape (n_samples,) Xyarray-like of shape (n_samples,) or (n_samples, n_tar..

enet_path sklearn.linear_model.enet_path(X, y, *, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, precompute='auto', Xy=None, copy_X=True, coef_init=None, verbose=False, return_n_iter=False, positive=False, check_input=True, **params) enet_path 파라미터 X{array-like, sparse matrix} of shape (n_samples, n_features) y{array-like, sparse matrix} of shape (n_samples,) or (n_samples, n_targets) l1_ra..

PassiveAggressiveRegressor sklearn.linear_model.PassiveAggressiveRegressor(*, C=1.0, fit_intercept=True, max_iter=1000, tol=0.001, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False) PassiveAggressiveRegressor 파라미터 Cfloat, 기본값=1.0 fit_interceptbool, 기본값=True max_it..

GammaRegressor class sklearn.linear_model.GammaRegressor(*, alpha=1.0, fit_intercept=True, max_iter=100, tol=0.0001, warm_start=False, verbose=0) GammaRegressor 파라미터 alphafloat, default=1 fit_interceptbool, default=True max_iterint, default=100 tolfloat, default=1e-4 warm_startbool, default=False verboseint, default=0

sklearn.linear_model.TweedieRegressor 파라미터 class sklearn.linear_model.TweedieRegressor(*, power=0.0, alpha=1.0, fit_intercept=True, link='auto', max_iter=100, tol=0.0001, warm_start=False, verbose=0) sklearn.linear_model.TweedieRegressor 파라미터 powerfloat, default=0 The power determines the underlying target distribution according to the following table: PowerDistribution 0 Normal 1 Poisson (1,2) ..

PoissonRegressor class sklearn.linear_model.PoissonRegressor(*, alpha=1.0, fit_intercept=True, max_iter=100, tol=0.0001, warm_start=False, verbose=0) PoissonRegressor 파라미터 alphafloat, default=1 fit_interceptbool, default=True max_iterint, default=100 tolfloat, default=1e-4 warm_startbool, default=False verboseint, default=0

TheilSenRegressor class sklearn.linear_model.TheilSenRegressor(*, fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=None, verbose=False) TheilSenRegressor 파라미터 fit_interceptbool, default=True copy_Xbool, default=True max_subpopulationint, default=1e4 n_subsamplesint, default=None max_iterint, default=300 tolfloat, de..

RANSACRegressor class sklearn.linear_model.RANSACRegressor(base_estimator=None, *, min_samples=None, residual_threshold=None, is_data_valid=None, is_model_valid=None, max_trials=100, max_skips=inf, stop_n_inliers=inf, stop_score=inf, stop_probability=0.99, loss='absolute_error', random_state=None) RANSACRegressor 파라미터 base_estimatorobject, default=None min_samplesint (>= 1) or float ([0, 1]), de..