What are the key evaluation criteria for AI models?

What are the key evaluation criteria for AI models?

The key evaluation criteria for AI models include accuracy, precision, recall, F1 score, and AUC-ROC.

Can you explain what precision and recall mean?

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

How do I choose the right evaluation metric for my model?

Choosing the right metric depends on your specific use case. For example, use precision for imbalanced classes.

What is F1 score and why is it important?

The F1 score is the harmonic mean of precision and recall, providing a balance between the two, especially in imbalanced datasets.