Submissions are scored on log loss:
$$
\textrm{LogLoss} = - \frac{1}{n} \sum_{i=1}^n \left[ y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)\right],
$$
where

- n is the number of videos being predicted
- \\( \hat{y}_i \\) is the predicted probability of the video being FAKE
- \\( y_i \\) is 1 if the video is FAKE, 0 if REAL
- \\( log() \\) is the natural (base e) logarithm

A smaller log loss is better. The use of the logarithm provides extreme punishments for being both confident and wrong. In the worst possible case, a prediction that something is true when it is actually false will add infinite to your error score. In order to prevent this, predictions are bounded away from the extremes by a small value.

## Submission File
For each `filename` in the test set, you must predict a probability for the `label` variable. The file should contain a header and have the following format:

    filename,label
    10000.mp4,0
    10001.mp4,0.5
    10002.mp4,1
    etc.