What are the different methods for evaluating AI performance?

What are the different methods for evaluating AI performance?

There are several methods for evaluating AI performance, including accuracy, precision, recall, F1 score, and ROC-AUC.

Can you explain what precision and recall mean?

Sure! Precision measures the accuracy of positive predictions, while recall measures the ability to find all relevant instances.

How do I choose the right evaluation method for my AI model?

Choosing the right method depends on your specific use case, the type of data, and the importance of false positives vs. false negatives.

What is the F1 score and why is it important?

The F1 score is the harmonic mean of precision and recall, providing a balance between the two. It's important for imbalanced datasets.