Your submissions will be evaluated using the **F1 Score with micro averaging**, also known as **F1-micro**.

The F1 Score is the harmonic mean of **precision** and **recall**. Micro-averaging computes these metrics **globally** by counting the total true positives, false negatives, and false positives across all classes.

**F1-micro formula:**

F1_micro = (2 × Precision_micro × Recall_micro) / (Precision_micro + Recall_micro)

Where:

- Precision_micro = Total True Positives / (Total True Positives + Total False Positives)  
- Recall_micro = Total True Positives / (Total True Positives + Total False Negatives)

You can calculate the F1-micro score in Python using `scikit-learn`:

```python

from sklearn.metrics import f1_score

f1_score(y_true, y_pred, average="micro")

```