Kaggle calculates the mean F-score based on comparison of your model predictions with the ground truth.

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. Precision is the ratio of true positives tp to all predicted positives tp+fp. Recall is the ratio of true positives to all actual positives tp+fn. The F1 score is given by:
\[ F1 = 2\frac{p \cdot r}{p+r}\ \ \mathrm{where}\ \ p = \frac{tp}{tp+fp},\ \ r = \frac{tp}{tp+fn} \]
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.