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sklearn.linear_model.orthogonal_mp_gram 파라미터<Python>/[Sklearn] 2022. 1. 14. 22:17
orthogonal_mp_gram sklearn.linear_model.orthogonal_mp_gram(Gram, Xy, *, n_nonzero_coefs=None, tol=None, norms_squared=None, copy_Gram=True, copy_Xy=True, return_path=False, return_n_iter=False) orthogonal_mp_gram 파라미터 Gramndarray of shape (n_features, n_features) 입력 데이터의 그램 행렬입니다. X.T * X입니다. Xyndarray of shape (n_features,) or (n_features, n_targets) 입력 대상에 X: X를 곱합니다.T * y입니다. n_nonzero_coefsi..
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sklearn.linear_model.orthogonal_mp 파라미터<Python>/[Sklearn] 2022. 1. 14. 18:01
orthogonal_mp sklearn.linear_model.orthogonal_mp(X, y, *, n_nonzero_coefs=None, tol=None, precompute=False, copy_X=True, return_path=False, return_n_iter=False) orthogonal_mp 파라미터 Xndarray of shape (n_samples, n_features) yndarray of shape (n_samples,) or (n_samples, n_targets) n_nonzero_coefsint, 기본값=None 솔루션에서 0이 아닌 원하는 항목 수입니다. 없음(기본값)인 경우 이 값은 n_features의 10%로 설정됩니다. tolfloat, 기본값=None 잔차의 최..
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sklearn.linear_model.lasso_path 파라미터<Python>/[Sklearn] 2022. 1. 13. 21:05
lasso_path sklearn.linear_model.lasso_path(X, y, *, 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, **params) lasso_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) epsfloat, 기본값=1e-3 n_alphasint, 기본..
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sklearn.linear_model.lars_path_gram 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:55
lars_path_gram sklearn.linear_model.lars_path_gram(Xy, Gram, *, n_samples, 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_gram 파라미터 Xyarray-like of shape (n_samples,) or (n_samples, n_targets) Xy = np.dot(X.T, y). Gramarray-like of shape (n_features, n_features) Gram = np..
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sklearn.linear_model.lars_path 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:53
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..
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sklearn.linear_model.enet_path 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:51
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..
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sklearn.linear_model.PassiveAggressiveRegressor 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:49
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..
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sklearn.linear_model.GammaRegressor 파라미터<Python>/[Sklearn] 2022. 1. 13. 20:45
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