Sklearn LogisticRegression Without Regularization

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

Solution 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.

Update: In sklearn versions 0.21 and higher, you can disable regularization by passing in penalty='none'. Check out the documentation here.

Solution 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.

Solution 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'.