小编jon*_*ans的帖子

我正在拟合逻辑回归模型,并将随机状态设置为固定值.

每次我做一个"适合"我得到不同的系数,例如:

classifier_instance.fit(train_examples_features, train_examples_labels)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, penalty='l2', random_state=1, tol=0.0001)

>>> classifier_instance.raw_coef_
array([[ 0.071101940040772596  ,  0.05143724979709707323,  0.071101940040772596  , -0.04089477198935181912, -0.0407380696457252528 ,  0.03622160087086594843,  0.01055345545606742319,
         0.01071861708285645406, -0.36248634699444892693, -0.06159019047096317423,  0.02370064668025737009,  0.02370064668025737009, -0.03159781822495803805,  0.11221150783553821006,
         0.02728295348681779309,  0.071101940040772596  ,  0.071101940040772596  ,  0.                    ,  0.10882033432637286396,  0.64630314505709030026,  0.09617956519989406816,
         0.0604133873444507169 ,  0.                    ,  0.04111685986987245051,  0.                    ,  0.                    ,  0.18312324521915510078,  0.071101940040772596  ,
         0.071101940040772596  ,  0.                    , -0.59561802045324663268, -0.61490898457874587635,  1.07812569991461248975,  0.071101940040772596  ]])

classifier_instance.fit(train_examples_features, train_examples_labels)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, penalty='l2', random_state=1, tol=0.0001)

>>> classifier_instance.raw_coef_
array([[ 0.07110193825129411394,  0.05143724970282205489,  0.07110193825129411394, -0.04089477178162870957, …

可以根据现有系数创建这样的实例,这些系数是在不同的实现(例如Java)中计算的吗？

我尝试创建一个实例,然后直接设置coef_和intercept_,它似乎工作,但我不确定这里是否有一个缺点或我是否可能会破坏某些东西.