figure - sample roc curve (sourcehtml#roc-metricsfigure - depicts the sample roc curve in generalthe roc curve is an important tool for visually interpreting classification models but it doesn' directly provide us with numerical value that we can use to compare models the metric which does that task is the area under curve popularly known as auc in the roc plot in figure - the area under the orange line is the area under the classifier' roc curve the ideal classifier will have the unit area under the curve based on this value we can compare two modelsgenerally the model with the auc score is better one we have built generic function for plotting roc curves with auc scores for binary as well as multi-class classification problems in our model_evaluation_utils module do check out the functionplot_model_roc_curveto know more about it the following code plots the roc curve for our breast cancer logistic regression model leveraging the same function meu plot_model_roc_curve(clf=logisticfeatures=x_testtrue_labels=y_test