Evaluation metric: ROC-AUC.

AUC - ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1. By analogy, the Higher the AUC, the better the model is at distinguishing between patients with the disease and no disease.

![ROC-AUC](https://miro.medium.com/max/451/1*pk05QGzoWhCgRiiFbz-oKQ.png)

The ROC curve is plotted with TPR against the FPR where TPR is on the y-axis and FPR is on the x-axis.

**Confusion matrix:**
```markdown
True positive (TP) |     False positive (FP)
--------------------------------------------
False negative (FN) |     True negative (TN)
```
---

**TPR = TP/(TP+FN)**

**FRR = FP/(TN+FP)**

`from sklearn.metrics import roc_auc_score`

## Submission Format

Submission Format is csv.

The file should contain a header and have the following format:
```
AuthorId,DuplicateAuthorIds
1,1
```