I just finished the first course and I have a question regarding Regularization. In all the assignments we applied the regularization by our own function, but if I want to use logisticregression in sklearn, how should I apply the concept ?

I noticed that the function has the terms penalty and C, are these the terms that we need to add? If yes, what are L1 and L2 in the penalty parameter? What is C ? Is it 1/lambda ?

Thanks

yes, itâ€™s C and C is 1/lambda. The regularization we have learnt in the course is the L2 regularization, and it adds \lambda w^2 to the cost. L1 regularization adds instead \lambda|w| to the cost. When combined, it adds \lambda_2w^2 + \lambda_1|w| to the cost.

Read this sklearn guide for the formulae. Read this sklearn experiment for the comparison between L1 and L2 regularization.