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Receiver Operating Characteristics (ROC) analysis is a useful method for evaluating classification models. ROC curves provide a means to compare individual models and determine thresholds which yield a high proportion of positive hits.
You can use ROC to gain 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.
ROC curves are similar to lift charts in that they provide a means of comparison between individual models and determine thresholds which yield a high proportion of positive hits.
The correct value for ROC threshold depends on the problem that the model is trying to solve.
ROC compares predicted and actual target values in a classification model. 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. For more information, see How to Use ROC