The evaluation metric for this competition is [F1 score](https://en.wikipedia.org/wiki/F-score). Higher F1 score will be ranked higher in the final ranking. The F1 score, commonly used in information retrieval, measures accuracy using the statistics [precision and recall](https://en.wikipedia.org/wiki/Precision_and_recall).

Precision is the ratio of true positives to all predicted positives. Recall is the ratio of true positives to all actual positives. The F1 score is given by `2 * (precision * recall)/(precision + recall)`.

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 files  (*.csv) should contain two columns: `id` and `class`. `class` is the labels of the test dataset predicted by the model.

The file should contain a header and have the following format:
```
id, class
1,1
2,0
3,1
```