sklearn.linear_model.LogisticRegression 파라미터

linear_model.LogisticRegression 파라미터

 

 

linear_model.LogisticRegression 파라미터

penalty{‘l1’, ‘l2’, ‘elasticnet’, ‘none’}, default=’l2’

dualbool, default=False

tolfloat, default=1e-4

Cfloat, default=1.0

fit_interceptbool, default=True

intercept_scalingfloat, default=1

class_weightdict or ‘balanced’, default=None

random_stateint, RandomState instance, default=None

solver{‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, default=’lbfgs’

max_iterint, default=100

multi_class{‘auto’, ‘ovr’, ‘multinomial’}, default=’auto’

verboseint, default=0

warm_startbool, default=False

n_jobsint, default=None

l1_ratiofloat, default=None