Sklearn的下垂和LBFG是否会惩罚拦截？
我们知道，在Sklearn实施中进行惩罚是我们必须处理的“设计错误”。一项工作是根据文档：
注意！合成特征重量作为所有其他特征都需要L1/L2正则化。为了减少正则化对合成特征重量的影响（因此在截距上）必须增加截距。
必须增加。
但是，同一文档说此参数仅在solver ='liblinear'时才有用。
我的问题：
其他求解器会惩罚拦截吗？我试图查看来源，我认为他们不确定，但我不确定，在任何地方都找不到明确的答案。
We know that penalizing intercept in sklearn implementation is a "design mistake" that we have to deal with. One work around is to set intercept_scaling to a very large number, per the documentation:
Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.
However, same documentation says that this parameter is useful only when solver='liblinear'.
My question:
Do other solvers penalise the intercept? I tried to look at the source and I think they don't but I am not sure and I couldn't find clear answer anywhere.
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logisticRegress的唯一求解器是“ liblinear”。官方文档>
参见 //i.sstatic.net/cmhqj.png“ rel =” nofollow noreferrer“>
The only solver of
LogisticRegressionthat penalizes the intercept is "liblinear".See the official documentation: