## Evaluation

[Accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification) is also used as a statistical measure of how well a binary classification test correctly identifies or excludes a condition. 
- Accuracy is the proportion of correct predictions, both true positives and true negatives among the total number of cases examined.
- It compares estimates of pre- and post-test probability. 
- It is a parameter of the test. 
- The formula for quantifying binary accuracy is:

\[ Accuracy = \frac{TP + TN}{TP + TN + FP + FN}  \]

where TP = True positive; FP = False positive; TN = True negative; FN = False negative

## Submission

**For every author in the dataset**, submission files should contain two columns: `id` and `class_label`. The file should contain a header and have the following format:

```
id, class_label
1,   0
2,   1
3,   1
4,   0
5,   1
.    .
.    .
.    .
```