AUC ROC Curve

Analyticsvidhya0516 Jun, 2023Business

The AUC ROC curve, also known as the Receiver Operating Characteristic curve, is a graphical representation of the performance of a binary classification model. It is widely used in machine learning and statistics to evaluate and compare the effectiveness of different classification algorithms. The AUC (Area Under the Curve) represents the measure of separability between the model's true positive rate (sensitivity) and its false positive rate (1 - specificity). The curve is created by plotting the true positive rate against the false positive rate at various classification thresholds. The AUC ROC curve provides valuable insights into the discriminatory power of a classification model. A perfect classifier would have an AUC score of 1, indicating that it can perfectly distinguish between the positive and negative instances. On the other hand, a completely random or ineffective classifier would have an AUC score of 0.5, which represents the diagonal line in the ROC space.