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 to calculate precision and recall?

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 are both precision and recall important?

Both metrics are crucial because they provide insights into the model's performance, especially in imbalanced datasets.

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

You can improve them by tuning your model, using different algorithms, or adjusting the classification threshold.