clf fit(xyprint(np exp(clf intercept_)np exp(clf coef_ ravel()) def lr_model(clfx)return ( np exp(-(clf intercept_ clf coef_* ))print(" ( =- )= \tp( = )= "%(lr_model(clf- )lr_ model(clf )) ( =- )= ( = )= you might have noticed that scikit-learn exposes the first coefficient through the special field intercept_ if we plot the fitted modelwe see that it makes perfect sense given the dataapplying logistic regression to our postclassification problem admittedlythe example in the previous section was created to show the beauty of logistic regression how does it perform on the extremely noisy datacomparing it to the best nearest neighbour classifier ( as baselinewe see that it performs bit betterbut also won' change the situation whole lotmethod mean(scoresstddev(scoreslogreg = logreg = logreg = logreg =