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sklearn.linear_model.LassoLars class sklearn.linear_model.LassoLars(alpha=1.0, *, fit_intercept=True, verbose=False, normalize='deprecated', precompute='auto', max_iter=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, positive=False, jitter=None, random_state=None) sklearn.linear_model.LassoLars 파라미터 alphafloat, default=1.0 fit_interceptbool, default=True verbosebool or int, default=F..

sklearn.linear_model.LassoCV 파라미터 class sklearn.linear_model.LassoCV(*, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize='deprecated', precompute='auto', max_iter=1000, tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=None, positive=False, random_state=None, selection='cyclic') sklearn.linear_model.LassoCV 파라미터 epsfloat, default=1e-3 n_alphasint, default=100 alphasndarr..

sklearn.linear_model.Lasso 파라미터 class sklearn.linear_model.Lasso(alpha=1.0, *, fit_intercept=True, normalize='deprecated', precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic') sklearn.linear_model.Lasso 파라미터 alphafloat, default=1.0 fit_interceptbool, default=True normalizebool, default=False precomputebool or array-lik..

sklearn.linear_model.LarsCV 파라미터 class sklearn.linear_model.LarsCV(*, 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) sklearn.linear_model.LarsCV 파라미터 fit_interceptbool, default=True Whether to calculate the intercept for this model. If set to false, no intercept will be u..

sklearn.linear_model.Lars 파라미터 class sklearn.linear_model.Lars(*, fit_intercept=True, verbose=False, normalize='deprecated', precompute='auto', n_nonzero_coefs=500, eps=2.220446049250313e-16, copy_X=True, fit_path=True, jitter=None, random_state=None) sklearn.linear_model.Lars 파라미터 fit_interceptbool, default=True Whether to calculate the intercept for this model. If set to false, no intercept wi..

sklearn.linear_model.ElasticNetCV 파라미터 class sklearn.linear_model.ElasticNetCV(*, l1_ratio=0.5, eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, normalize='deprecated', precompute='auto', max_iter=1000, tol=0.0001, cv=None, copy_X=True, verbose=0, n_jobs=None, positive=False, random_state=None, selection='cyclic') sklearn.linear_model.ElasticNetCV 파라미터 l1_ratiofloat or list of float, de..

sklearn.linear_model.ElasticNet 파라미터 class sklearn.linear_model.ElasticNet(alpha=1.0, *, l1_ratio=0.5, fit_intercept=True, normalize='deprecated', precompute=False, max_iter=1000, copy_X=True, tol=0.0001, warm_start=False, positive=False, random_state=None, selection='cyclic') sklearn.linear_model.ElasticNet 파라미터 alphafloat, default=1.0 Constant that multiplies the penalty terms. Defaults to 1.0..

sklearn.linear_model.SGDRegressor 파라미터 class sklearn.linear_model.SGDRegressor(loss='squared_error', *, penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, random_state=None, learning_rate='invscaling', eta0=0.01, power_t=0.25, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, warm_start=False, average=Fa..