In this article, you will learn what a ROC curve is, how to interpret it, and why it is important for machine learning model validation. A ROC curve stands for receiver operating characteristic curve.

The AUC-ROC metric clearly helps determine and tell us about the capability of a model in distinguishing the classes. The judging criteria being - Higher the AUC, better the model. AUC-ROC curves are ...

The area under the ROC curve (AUC) measures the performance of a machine learning algorithm. ROC curves visualize the statistical accuracy of classifier selection.[1] ...

In this article, we will learn more about the ROC-AUC curve and how we make use of it to compare different machine learning models to select the best performing model. For this experiment, we will ...

Abstract: Traditionally, machine learning algorithms have been evaluated in applications ... measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC ...

Abstract: ROC curves have been used for a fair comparison of machine learning algorithms since the late 90's. Accordingly, the area under the ROC curve (AUC) is nowadays considered a relevant learning ...