## What is the F1 Score?

The F1 Score is a performance metric used to evaluate binary classification models. It is the harmonic mean of precision and recall, which are two measures of classification performance. The F1 Score ranges from 0 to 1, with 1 indicating perfect precision and recall and 0 indicating the worst possible performance. It is particularly useful for evaluating models on imbalanced datasets, as it takes into account both false positives and false negatives.

## Example of calculating the F1 Score using scikit-learn in Python:

```
from sklearn.metrics import f1_score
y_true = [0, 1, 1, 0, 1, 1]
y_pred = [0, 1, 0, 0, 1, 1]
f1 = f1_score(y_true, y_pred)
print("F1 Score:", f1)
```

In this example, we use the scikit-learn library to calculate the F1 Score for a binary classification problem with sample ground-truth labels and predictions.

## Resources

To learn more about the F1 Score and its applications in machine learning, you can explore the following resources: