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AUC / Area under curve
Area under curve (AUC) is used to see how well your classifier can separate positive and negative examples.
AUC = 1, means the model’s predictions are perfect, but 0 is also good news because you just need to invert your model’s output to obtain a perfect model.
The AUC metric computes the area under a discretized curve of true positive versus false positive rates, Receiver Operating Characteristic curve.
AUC around 0.5 is the same thing as a random guess. The further away the AUC is from 0.5, the better. If AUC is below 0.5, then you may need to invert the decision your model is making.
AUC = 1, means the model’s predictions are perfect.
AUC = 0 is good news because you just need to invert your model’s output to obtain a perfect model.
This StackExchange post contains a nice explanation of the AUC.