问题
Logistic regression class in sklearn comes with L1 and L2 regularization. How can I turn off regularization to get the "raw" logistic fit such as in glmfit in Matlab? I think I can set C = large number but I don't think it is wise.
see for more details the documentation http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression
回答1:
Yes, choose as large a number as possible. In regularization, the cost function includes a regularization expression, and keep in mind that the C parameter in sklearn regularization is the inverse of the regularization strength.
C in this case is 1/lambda, subject to the condition that C > 0.
Therefore, when C approaches infinity, then lambda approaches 0. When this happens, then the cost function becomes your standard error function, since the regularization expression becomes, for all intents and purposes, 0.
回答2:
Go ahead and set C as large as you please. Also, make sure to use l2 since l1 with that implementation can be painfully slow.
回答3:
I got the same question and tried out the answer in addition to the other answers:
If set C to a large value does not work for you,
also set penalty='l1'.
来源：https://stackoverflow.com/questions/25427650/sklearn-logisticregression-without-regularization