## Evaluation metric

The evaluation metric for this competition is **Mean F1-Score**. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\(P\\) and recall \\(R\\).

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**Precision** is the ratio of true positives \\(p\_T\\) to all predicted positives \\( p\_{T} + p\_{F} \\), *i.e.*,
\\[ P = \dfrac{p\_T}{p\_{T} + p\_{F}} \\]

**Recall** is the ratio of true positives \\(p\_T\\) to all actual positives \\( p\_{T} + n\_{F} \\), where \\( n\_{F} \\) gives the number of false negatives. Thus, recall \\(R\\) is
\\[ R = \dfrac{p\_T}{p\_{T} + n\_{F}} \\]

The **F1 score** is given by
\\[ F1 = 2\frac{P \cdot R}{P+R} \\]

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

Your submission file should contain two columns: `id` (the patients' IDs) and `heart_disease` (binary).

The file should contain a header and have the following format:

`id` | `heart_disease`
|-----|-----|
|179 | 0 |
| 14 | 0 | 
|195 | 1 |
| ⋮ | ⋮ |