What is the difference between precision and recall in NLP models?

What is the difference between precision and recall in NLP models?

Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

Can you give me an example of how precision and recall are calculated?

Sure! If a model predicts 10 positive cases and 7 are correct, precision is 0.7. If there are 10 actual positives and 7 are found, recall is 0.7.

Why is it important to balance precision and recall?

Balancing precision and recall is crucial to avoid false positives and false negatives, ensuring the model is effective.

What metrics can I use to evaluate both precision and recall?

You can use the F1 score, which is the harmonic mean of precision and recall, to evaluate their balance.