Submissions are scored on the 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 rows in the test set
 - \\( \hat{y}\_i \\) is the predicted probability the `Class` is a pulsar
 - \\( y\_i  \\) is 1 if `Class` is pulsar, otherwise 0
 - \\( log \\) is the natural logarithm

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 an infinite amount to your error score. In order to prevent this, predictions are bounded away from the extremes by a small value.

## Submission File
For each `id` in the test set, you must predict the value for the target `Class`. The file should contain a header and have the following format:

    id,Strength
    117564,0.11
    117565,0.32
    117566,0.95
    etc.