on the left-hand side graphwe see the / curve for an ideal classifierwe would have the curve going from the top-left corner directly to the top-right corner and then to the bottom-right cornerresulting in an area under curve (aucof the right-hand side graph depicts the corresponding roc curve it plots the true positive rate over the false positive rate herean ideal classifier would have curve going from the lower-left to top-left corner and then to the top-right corner random classifier would be straight line from the lower-left to upper-right corneras shown by the dashed line having an auc of thereforewe cannot compare the auc of / curve with that of an roc curve when comparing two different classifiers on the same datasetwe are always safe to assume that higher auc of / curve for one classifier also means higher auc of the corresponding roc curve and vice versa thereforewe never bother to generate both more on this can be found in the very insightful paper the relationship between precision-recall and roc curvesjesse davis and mark goadrichicml the definitions of both the curvesx and axes are given in the following tablex axis axis / roc looking at the definitions of both curvesx axes and axeswe see that the true positive rate in the roc curve' axis is the same as recall of the / graph' axis the false positive rate measures the fraction of true negative examples that were falsely identified as positive onesgiving in perfect case (no false positivesand otherwise contrast this to the precision curvewhere we track exactly the oppositenamely the fraction of true positive examples that we correctly classified as such going forwardlet us use roc curves to measure our classifier' performance to get better feeling for it the only challenge for our multiclass problem is that both roc and / curves assume binary classification problem for our purposelet us therefore create one chart per genre that shows how the classifier performed "one versus restclassificationy_pred clf predict(x_testfor label in labels