The evaluation metric for this competition is [**Mean F1-Score**](https://en.wikipedia.org/wiki/F-score). The **F1 score**, commonly measures accuracy using the precision(p) and recall(r).

Precision is the ratio of true positives(tp) to all predicted positives (tp + fp). Recall is the ratio of true positives (tp) to all actual positives (tp + fn). 

The F1 score is given by:
$$ F1 = 2\frac{p \cdot r}{p+r}\ \ \mathrm{where}\ \ p = \frac{tp}{tp+fp},\ \ r = \frac{tp}{tp+fn} $$

The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

## Submission Format

submission file should contain a header and have the following format:
```
review_id,rating
5c4df7e70e9b438c761f07a4620ccb7c,0
8eaeaf13213eeb16ad879a2a2591bbe5,5
dce649b733c153ba5363a0413cac988f,4
8a46df0bb997269d6834f9437a4b0a77,2
```