What is F1 Score in NLP?

Can you explain what the F1 score is in NLP?

Sure! The F1 score is a measure of a model's accuracy in binary classification, balancing precision and recall.

How is the F1 score calculated?

The F1 score is calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall).

What are precision and recall?

Precision is the ratio of true positive predictions to the total predicted positives, while recall is the ratio of true positives to actual positives.

Why is the F1 score important?

The F1 score is important because it provides a single metric that balances both precision and recall, especially in imbalanced datasets.