What is the difference between precision and recall in machine learning?

What is the difference between precision and recall in machine learning?

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 used?

Sure! In a spam detection system, precision is the percentage of correctly identified spam emails, while recall is the percentage of actual spam emails that were identified.

Why is it important to balance precision and recall?

Balancing precision and recall is crucial because high precision with low recall means missing many relevant instances, while high recall with low precision means many false positives.

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

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