What is the difference between precision, recall, and accuracy?

What is the difference between precision, recall, and accuracy?

Precision measures the accuracy of positive predictions, recall measures the ability to find all positive instances, and accuracy is the overall correctness of the model.

Can you give me an example of how these metrics are used?

Sure! In a medical test for a disease, precision would indicate how many of the positive test results were true positives, while recall would show how many actual cases were detected.

Why is it important to consider all three metrics?

Considering all three metrics helps to understand the model's performance comprehensively, especially in imbalanced datasets.

What happens if I only focus on accuracy?

Focusing solely on accuracy can be misleading, especially if the dataset is imbalanced, as it may not reflect the model's true performance.