Logistic Regression in sklearn

In sklearn, the objective function for binary logistic regression expresses the trade-off be- tween training error and model complexity through a parameter C that is multiplied with the error term, as shown below. See the sklearn documentation at http://scikit-learn. org/stable/modules/linear_model.html#logistic-regression. N ww+C In(en (wxn). E(w) = ww +1) Show that the sum in the second term is equal with the negative log-likelihood. (2) Compute the C parameter such that the objective is equivalent with the standard for- mulation shown on the slides in which the regularization parameter a or A is multiplied with the L2 norm term.