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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..

QuantileRegressor class sklearn.linear_model.QuantileRegressor(*, quantile=0.5, alpha=1.0, fit_intercept=True, solver='interior-point', solver_options=None) QuantileRegressor 파라미터 quantilefloat, default=0.5 alphafloat, default=1.0 fit_interceptbool, default=True solver{‘highs-ds’, ‘highs-ipm’, ‘highs’, ‘interior-point’, ‘revised simplex’}, default=’interior-point’ solver_optionsdict, default=None

HuberRegressor class sklearn.linear_model.HuberRegressor(*, epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05) HuberRegressor 파라미터 epsilonfloat, greater than 1.0, default=1.35 max_iterint, default=100 alphafloat, default=0.0001 warm_startbool, default=False fit_interceptbool, default=True tolfloat, default=1e-05

MultiTaskLassoCV class sklearn.linear_model.MultiTaskLassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize='deprecated', max_iter=1000, tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=None, random_state=None, selection='cyclic') MultiTaskLassoCV 파라미터 epsfloat, default=1e-3 n_alphasint, default=100 alphasarray-like, default=None fit_interceptbool, default=True nor..

MultiTaskLasso class sklearn.linear_model.MultiTaskLasso(alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') MultiTaskLasso 파리미터 alphafloat, default=1.0 fit_interceptbool, default=True normalizebool, default=False copy_Xbool, default=True max_iterint, default=1000 tolfloat, default=1e-4 warm_st..