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Reciever Operating Characteristics (ROC) is a method for experimenting with changes in the probability threshold and observing the resulting effect on the predictive power of the model.
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. ROC was originally used in signal detection theory to gauge the true hit versus false alarm ratio when sending signals over a noisy channel.
The horizontal axis of an ROC graph measures the false positive rate as a percentage. The vertical axis shows the true positive rate. The top left hand corner is the optimal location in an ROC curve, indicating high TP (true-positive) rate versus low FP (false-positive) rate.
The area under the ROC curve measures the discriminating ability of a binary classification model. The larger the area under the curve, the higher the likelihood that an actual positive case is assigned a higher probability of being positive than an actual negative case. The area under the curve measure is especially useful for data sets with unbalanced target distribution (one target class dominates the other).