Submissions are evaluated using the Macro-Averaged F1 Score, which is the mean of the F1 scores calculated for each class. The F1 score, a measure of a test's accuracy, is calculated using the formula:

$$
F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}
$$​
 
Where:
- Precision is the ratio of correctly predicted positive observations to the total predicted positives.
- Recall is the ratio of correctly predicted positive observations to all observations in the actual class.


The Macro-Averaged F1 Score is then computed as:

​$$ F1_{macro} = \frac{1}{C} \sum_{c=1}^{C} F1_c $$
 
Where:
- C is the total number of classes (in this case, 2: up or not up).
- F1_c is the F1 score for class c.

This evaluation metric ensures that the model performs well across both classes, balancing the precision and recall to provide a comprehensive assessment of its accuracy.