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 positive instances.

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

Sure! If you have 100 positive cases and your model predicts 80 as positive, with 70 true positives, precision is 70/80 = 0.875, and recall is 70/100 = 0.7.

Why is it important to consider both precision and recall?

Considering both helps balance the trade-off between false positives and false negatives, which is crucial depending on the application.

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

You can improve them by tuning your model, using better features, or applying techniques like resampling or threshold adjustment.