Machine Learning, Heartbeat.
Rate') plt.ylabel('True Positive Rate') plt.title('ROC Curve') plt.show() auc = roc_auc_score(y_true, y_score) print("AUC:", auc) The ROC curve is useful when the classes are imbalanced and the cost of false positives and false negatives is not the same. For example, it can be used to evaluate the