Do Sklearn's SAG and lbfgs penalize the intercept?
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.
The only solver of LogisticRegression that penalizes the intercept is "liblinear".
See the official documentation:
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