How to perform an unregularized logistic regression using scikit-learn?

Question

From scikit-learn's documentation, the default penalty is "l2", and C (inverse of regularization strength) is "1". If I keep this setting penalty='l2' and C=1.0, does it mean the training algorithm is an unregularized logistic regression? In contrast, when C is anything other than 1.0, then it's a regularized logistic regression classifier?

Answer 1

No, it's not like that.

Let's have a look at the definitions within sklearn's user-guide:

We see:

• C is multiplied with the loss while the left-term (regularization) is untouched

This means:

• Without modifying the code you can never switch-off the regularization completely
• But: you can approximately switch-off regularization by setting C to a huge number!
  • As the optimization tries to minimize the sum of regularization-penalty and loss, increasing C decreases the relevance of the regularization-penalty