The F1 score is used exclusively for regression models to balance the impact of outliers.

The F1 score is a weighted average of precision and recall, providing a single metric that balances both false positives and false negatives.

The F1 score is identical to accuracy and can be used interchangeably in all classification problems.

The F1 score is only applicable when the dataset is perfectly balanced.