What is the F1 score in NLP models?

What is the F1 score in NLP models?

The F1 score is a measure of a model's accuracy that considers both precision and recall. It's particularly useful in NLP.

How is the F1 score calculated?

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

Why is the F1 score important?

The F1 score is important because it provides a balance between precision and recall, especially in imbalanced datasets.

Can you give an example of F1 score in action?

Sure! If a model predicts 80 true positives, 10 false positives, and 10 false negatives, the F1 score would be calculated as follows: precision = 80/(80+10) = 0.89, recall = 80/(80+10) = 0.89, and F1 = 2 * (0.89 * 0.89) / (0.89 + 0.89) = 0.89.