ROC Curve and AUC -- Explained

ROC (receiver operating characteristics) curve and AOC (area under the curve) are performance measures that provide a comprehensive evaluation of classification models. AUC turns the ROC curve into a numeric representation of performance for a binary classifier. AUC is the area under the ROC curve and takes a value between 0 and 1. AUC indicates how successful a model is at separating positive and negative classes. Before going in detail, let's first explain the confusion matrix and how different threshold values change the outcome of it. A confusion matrix is not a metric to evaluate a model, but it provides insight into the predictions.