What are the key metrics for evaluating AI performance?

What are the key metrics for evaluating AI performance?

Key metrics include accuracy, precision, recall, F1 score, and AUC-ROC. Each serves a different purpose.

Can you explain the difference between precision and recall?

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

Why is the F1 score important?

The F1 score balances precision and recall, providing a single metric to evaluate model performance, especially in imbalanced datasets.

What is AUC-ROC and why should I use it?

AUC-ROC evaluates a model's ability to distinguish between classes across different thresholds, useful for binary classification.