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
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.
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.
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