What are the key evaluation metrics for AI models?

What are the key evaluation metrics for AI models?

Key evaluation metrics for AI models include accuracy, precision, recall, F1 score, and AUC-ROC.

Can you explain the difference between precision and recall?

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

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, especially in imbalanced datasets.

How do I choose the right metric for my AI project?

Choosing the right metric depends on your project's goals, such as whether you prioritize false positives or false negatives.