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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..
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sklearn.linear_model.OrthogonalMatchingPursuitCV 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:51
linear_model.OrthogonalMatchingPursuitCV class sklearn.linear_model.OrthogonalMatchingPursuitCV(*, copy=True, fit_intercept=True, normalize='deprecated', max_iter=None, cv=None, n_jobs=None, verbose=False) linear_model.OrthogonalMatchingPursuitCV 파라미터 copybool, default=True fit_interceptbool, default=True normalizebool, default=True max_iterint, default=None cvint, cross-validation generator or ..
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sklearn.linear_model.OrthogonalMatchingPursuit 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:50
linear_model.OrthogonalMatchingPursuit class sklearn.linear_model.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize='deprecated', precompute='auto') sklearn.linear_model.OrthogonalMatchingPursuit 파라미터 n_nonzero_coefsint, default=None tolfloat, default=None fit_interceptbool, default=True normalizebool, default=True precompute‘auto’ or bool, default=’auto ’
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sklearn.linear_model.LassoLarsIC 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:49
sklearn.linear_model.LassoLarsIC class sklearn.linear_model.LassoLarsIC(criterion='aic', *, fit_intercept=True, verbose=False, normalize='deprecated', precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, positive=False, noise_variance=None) sklearn.linear_model.LassoLarsIC 파라미터 criterion{‘aic’, ‘bic’}, default=’aic’ fit_interceptbool, default=True verbosebool or int, default=..
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sklearn.linear_model.LassoLarsCV 파라미터<Python>/[Sklearn] 2022. 1. 11. 18:47
sklearn.linear_model.LassoLarsCV class sklearn.linear_model.LassoLarsCV(*, fit_intercept=True, verbose=False, max_iter=500, normalize='deprecated', precompute='auto', cv=None, max_n_alphas=1000, n_jobs=None, eps=2.220446049250313e-16, copy_X=True, positive=False) sklearn.linear_model.LassoLarsCV 파라미터 fit_interceptbool, default=True verbosebool or int, default=False max_iterint, default=500 norma..