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Receiver Operating Characteristics (ROC) analysis is a useful method for evaluating Classification models. ROC applies to binary classification only. ROC is plotted as a curve. The area under the ROC curve measures the discriminating ability of a binary Classification model. The correct value for the ROC threshold depends on the problem that the model is trying to solve.
ROC curves are similar to lift charts in that they provide a means of comparison between individual models and determine thresholds that yield a high proportion of positive results. An ROC curve does the following:
Provides a means to compare individual models and determine thresholds that yield a high proportion of positive results.
Provides insight into the decision-making ability of the model. For example, you can determine how likely the model is to accurately predict the negative or the positive class.
Compares predicted and actual target values in a Classification model.