What are the best metrics to test AI performance?

What are the best metrics to test AI performance?

The best metrics depend on the type of AI model. For classification, accuracy, precision, recall, and F1 score are common. For regression, you might use mean squared error or R-squared.

Can you explain precision and recall?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances. High precision means fewer false positives, and high recall means fewer false negatives.

How do I choose the right metric for my project?

Consider the goals of your project. If false positives are costly, prioritize precision. If missing positive cases is critical, focus on recall.

What about F1 score?

The F1 score is the harmonic mean of precision and recall. It's useful when you need a balance between the two, especially in imbalanced datasets.