The evaluation metric for this competition is
[AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)
.
AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example.
AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0.
**AUC is desirable for the following two reasons:**

1. AUC is scale-invariant. It measures how well predictions are ranked, rather than their absolute values.
2. AUC is classification-threshold-invariant. It measures the quality of the model's predictions irrespective of what classification threshold is chosen.