전체 글
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sklearn.linear_model.QuantileRegressor 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:35
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
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sklearn.linear_model.HuberRegressor 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:34
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
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sklearn.linear_model.MultiTaskLassoCV 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:33
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..
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sklearn.linear_model.MultiTaskLasso 파리미터<Python>/[Sklearn] 2022. 1. 13. 20:31
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..
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sklearn.linear_model.MultiTaskElasticNetCV 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:30
MultiTaskElasticNetCV class sklearn.linear_model.MultiTaskElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize='deprecated', max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, random_state=None, selection='cyclic') MultiTaskElasticNetCV 파라미터 l1_ratiofloat or list of float, default=0.5 epsfloat, default=1e-3 n_alphasint, default=1..
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sklearn.linear_model.MultiTaskElasticNet 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:55
sklearn.linear_model.MultiTaskElasticNet 파라미터 class sklearn.linear_model.MultiTaskElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, random_state=None, selection='cyclic') sklearn.linear_model.MultiTaskElasticNet 파라미터 alphafloat, default=1.0 l1_ratiofloat, default=0.5 fit_interceptbool, default=True normali..
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sklearn.linear_model.BayesianRidge 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:54
sklearn.linear_model.BayesianRidge 파라미터 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, alpha_init=None, lambda_init=None, compute_score=False, fit_intercept=True, normalize='deprecated', copy_X=True, verbose=False) sklearn.linear_model.BayesianRidge 파라미터 n_iterint, default=300 tolfloat, default=1e-3 alpha_1float, d..
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sklearn.linear_model.ARDRegression 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:52
sklearn.linear_model.ARDRegression 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, threshold_lambda=10000.0, fit_intercept=True, normalize='deprecated', copy_X=True, verbose=False) linear_model.ARDRegression 파라미터 n_iterint, default=300 tolfloat, default=1e-3 alpha_1float, default=1e-6 alpha_2flo..