What is the difference between precision and recall in AI?

What is the difference between precision and recall in AI?

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

Can you give me an example of precision and recall in a classification task?

Sure! In a spam detection model, precision is the percentage of correctly identified spam emails out of all emails marked as spam. Recall is the percentage of actual spam emails that were correctly identified.

Why are both precision and recall important?

Both metrics are crucial because they provide a balanced view of a model's performance. High precision means fewer false positives, while high recall means fewer false negatives.

How can I improve both precision and recall in my model?

You can improve them by tuning your model's thresholds, using better features, or employing techniques like cross-validation.