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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..

linear_model.RidgeCV 파라미터 class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), *, fit_intercept=True, normalize='deprecated', scoring=None, cv=None, gcv_mode=None, store_cv_values=False, alpha_per_target=False) linear_model.RidgeCV 파라미터 alphasndarray of shape (n_alphas,), default=(0.1, 1.0, 10.0) fit_interceptbool, default=True normalizebool, default=False scoringstr, callable, default=No..

linear_model.Ridge class sklearn.linear_model.Ridge(alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, solver='auto', positive=False, random_state=None) ||y - Xw||^2_2 + alpha * ||w||^2_2 linear_model.Ridge 파라미터 alpha{float, ndarray of shape (n_targets,)}, default=1.0 fit_interceptbool, default=True normalizebool, default=False copy_Xbool, default=Tr..

linear_model.LinearRegression class sklearn.linear_model.LinearRegression(*, fit_intercept=True, normalize='deprecated', copy_X=True, n_jobs=None, positive=False) linear_model.LinearRegression 파라미터 fit_interceptbool, default=True normalizebool, default=False copy_Xbool, default=True n_jobsint, default=None positivebool, default=False

sklearn.linear_model.SGDOneClassSVM 파라미터 class sklearn.linear_model.SGDOneClassSVM(nu=0.5, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, warm_start=False, average=False) linear_model.SGDOneClassSVM 파라미터 nufloat, default=0.5 fit_interceptbool, default=True max_iterint, default=1000 tolfloat or None, defaul..

linear_model.SGDClassifier class sklearn.linear_model.SGDClassifier(loss='hinge', *, 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, n_jobs=None, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False, av..

linear_model.RidgeClassifierCV linear_model.RidgeClassifierCV 파라미터 alphasndarray of shape (n_alphas,), default=(0.1, 1.0, 10.0) fit_interceptbool, default=True normalizebool, default=False scoringstr, callable, default=None cvint, cross-validation generator or an iterable, default=None class_weightdict or ‘balanced’, default=None store_cv_valuesbool, default=False