Submissions are evaluated on [area under the ROC curve](http://en.wikipedia.org/wiki/Receiver_operating_characteristic) between the predicted probability and the observed target.

**AUC-ROC** stands for *Area Under the Receiver Operating Characteristic Curve*. It is a performance measurement used for classification problems, especially when dealing with imbalanced datasets.

**- ROC Curve:** The ROC curve is a graphical representation of a model's true positive rate (sensitivity) versus its false positive rate (1-specificity) across different threshold settings.

**- AUC: **The Area Under the Curve (AUC) quantifies the overall ability of the model to discriminate between the positive class (churned) and the negative class (not churned).

An AUC of:

- 0.5 indicates a model with no discrimination ability (random guessing).
- 1.0 indicates a perfect model.

>*In this competition, the higher the AUC-ROC score, the better the model is at correctly ranking customers by their likelihood of churning.*

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13666066%2Fb19553b503d5ce53e46a98ae2ece3c6f%2FScreenshot%202024-08-25%20030523.jpg?generation=1724537152529540&alt=media)