Submissions are scored on the log loss, also called the predictive binomial deviance:
$$
\textrm{LogLoss} = - \frac{1}{n} \sum_{i=1}^n \left[ y_i \log(\hat{y}_i) + (1 - y_i) \log(1 - \hat{y}_i)\right],
$$
where
n is the number of rows in sampleSubmissions, each row represents one impression, which is a unique combination of an ad and a search
\\( \hat{y}_i \\) is the predicted probability of this ad being clicked
\\( y_i \\) is 1 if the actual ad's IsClick = 1, 0 if IsClick = 0
\\( 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 of \\(1.0 * 10 ^ {-15} \\).
Submission File
The file should contain a header and have the following format:
TestId,IsClick
0,0
1,0
5,0
7,0
9,0
etc.