sklearn.linear_model.PassiveAggressiveRegressor 파라미터

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_iterint, 기본값=1000

tolfloat or None, 기본값=1e-3  

early_stoppingbool, 기본값=False

validation_fractionfloat, 기본값=0.1

n_iter_no_changeint, 기본값=5

shufflebool, 기본값=True

verboseint, 기본값=0

lossstr, 기본값=”epsilon_insensitive”  

epsilonfloat, 기본값=0.1

random_stateint, RandomState instance, 기본값=None  

warm_startbool, 기본값=False  

averagebool or int, 기본값=False